From payman@ebs330.eb.uah.edu Mon Jan 22 08:58:29 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 08:58:27 -0600; AA00791 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sun, 21 Jan 96 12:02:09 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa17418; 20 Jan 96 17:42:26 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa17416; 20 Jan 96 17:27:55 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa02200; 20 Jan 96 17:27:46 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa11410; 20 Jan 96 15:48:49 EST Received: from uahis1.uah.edu by RI.CMU.EDU id aa17004; 20 Jan 96 15:48:05 EST Received: from ebs330 by uahis1.uah.edu with SMTP ; Sat, 20 Jan 96 14:23:04 CST Received: from anahita.eb.uah.edu.uah.edu by ebs330 (4.1/SMI-4.1) id AA20275; Sat, 20 Jan 96 14:24:11 CST Date: Sat, 20 Jan 96 14:24:11 CST From: Payman Arabshahi Message-Id: <9601202024.AA20275@ebs330> To: Connectionists@cs.cmu.edu Subject: CIFEr'96 Oral & Poster Presentations IEEE/IAFE 1996 $$$$$$$$$$$ $$$$$$ $$$$$$$$$$$ $$$$$$$$$$ $$$$$$$$$$$ $$$$$$ $$$$$$$$$$$ $$$$$$$$$$ $$$$ $$ $$$$ $$$$ $$$ $$$ $$$$ $$$$ $$$$$$$ $$$$$$ $$$$$$$$$$ $$$$ $$$$ $$$$$$$ $$$$$$ $$$$$$$$$$ $$$$ $$ $$$$ $$$$ $$$ $$$ $$$ $$$$$$$$$$$ $$$$$$ $$$$ $$$$$$$$$$ $$$ $$$$$$$$$$$ $$$$$$ $$$$ $$$$$$$$$$ $$$ IEEE/IAFE Conference on Computational Intelligence for Financial Engineering March 24-26, 1996 Crowne Plaza Manhattan - New York City http://www.ieee.org/nnc/conferences/cfp/cifer96.html [next update: February 1] ORAL PRESENTATIONS ------------------ Financial Computing Environments -------------------------------- "New Computational Architectures for Pricing Derivatives" R. Freedman, R. DiGiorgio "CAFE: A Complex Adaptive Financial Environment" R. Even, B. Mishra "Financial Trading Center at the University of Texas" P. Jaillet Market Behavior Models ---------------------- "Neural Networks Prediction of Multivariate Financial Time Series: The Swiss Bond Case" T. Ankenbrand, M. Tomassini "Bridging the Gap Between Nonlinearity Tests and the Efficient Market Hypothesis by Genetic Programming" S. Chen, C. Yeh "Models of Market Behavior: Bringing Realistic Games to Market" S. Leven Chaos and Time Series for Financial Systems ------------------------------------------- "Impetus for Future Growth in the Globalization of Stock Investments: An Evidence from Joint Time Series and Chaos Analyses" M. Hoque "Finding Time Series Among the Chaos: Stochastics, Deseasonalization, and Texture-Detection using Neural Nets" P. Werbos "Financial Time Series Analysis and Forecasting Using Computer Simulation and Methods of Nonlinear Adaptive Control of Chaotic Systems" A. Fradhov, S. Fradhov, A. Markov, D. Oliva Neural Nets for Financial Applications -------------------------------------- "Experiments in Predicting the German Stock Index DAX with Density Estimating Neural Networks" D. Ormoneit, R. Neuneier "Stock Market Prediction Using Different Neural Network Classification Architectures" C. Dagli, K. Schierholt "Modelling Stock Return Sensitivities to Economic Factors with the Kalman Filter and Neural Networks" Y. Bentz, L. Boone, J. Connor Fuzzy Logic for Financial Applications -------------------------------------- "Computer Supported Determination of Bank Credit Conditions" S. Schwarze "Fuzzy Logic and Genetic Algorithms for Financial Risk Management" T. Rubinson, R. Yager "Foreign Exchange Rate Prediction by Fuzzy Inferencing on Deterministic Chaos" S. Ghoshray Financial Data Mining --------------------- "Stock Selection Combining Rule Generation and Risk/Reward Portfolio Optimization" C. Apte, S. Hong, A. King "Data Driven Risk Management System" R. Grossman "Intelligent Hybrid System for Data Mining" M. Hambaba Simulation Techniques for Derivatives Pricing --------------------------------------------- "Path Integral Monte Carlo Method and Maximum Entropy: A Complete Solution for the Derivative Valuation Problem" M. Makivic Problems with Monte Carlo Simulation in the Pricing of Contingent Claims" J. Molle, F. Zapatero "Faster Simulation of the Prices of Derivative Securities" S. Paskov Financial Time Series Prediction I ---------------------------------- "Automated Mathematical Modelling for Financial Time Series Prediction Using Fuzzy Logic, Dynamical Systems and Fractal Theory" O. Castillo, P. Melin "Max-Min Optimal Investing" E. Ordentlich, T. Cover "Building Long/Short Portfolios Using Rule Induction" G. John, P. Miller Financial Time Series Prediction II ----------------------------------- "Adaptive Rival Penalized Competitive Learning and Combined Linear Predictor with Application to Financial Investment" Y. Cheung, Z. Lai, L. Xu "A Rule-based Neural Stock Trading Decision Support System" S. Chou, C. Chen, C. Yang, F. Lai "The Gene Expression Messy Genetic Algorithm for Financial Applications" H. Kargupta, K. Buescher Term Structure Modeling ----------------------- "Analysing Shocks on the Interest Rates Structure with Kohonen Map" M. Cottrell, E. De Bodt, P. Gregoire, E. Henrion "Interest Rate Futures: Estimation of Volatility Parameters in an Arbitrage-Free Framework" R. Bhar, C. Chiarella "Prediction of Individual Bond Prices Via the TDM Model" T. Kariya, H. Tsuda Financial Market Volatility --------------------------- "Robust Estimation Analytics for Financial Risk Management" H. Green, R. Martin, M. Pearson "Implied Volatility Functions: Empirical Tests" B. Dumas, J. Fleming, R. Whaley "Evaluation of Common Models Used in the Estimation of Historical Volatility" J. Dalle Molle Business Decision Tools ----------------------- "Fuzzy Queries for Top-Management Succession Planning" T. Sutter, M. Schroder, R. Kruse, J. Gebhardt "Density Based Clustering and Radial Basis Function Modeling to Generate Credit Card Fraud Scores" V. Hanagandi, A. Dhar, K. Buescher "Nonlinear Analysis of Retail Performance" D. Vaccari POSTER PRESENTATIONS -------------------- "Fuzzy Set Methods for Uncertainty Representation in Risky Financial Decisions" R. Yager "Trading Mechanisms and Return Volatility: Empirical Investigation on Shang Hai Stock Exchange Based on a Neural Network Model" Z. Lai, Y. Chuang, L. Xu "Application of Fuzzy Regression Models to Predict Exchange Rates for Composite Currencies" S. Ghoshray "Risk Management in an Uncertain Environment by Fuzzy Statistical Methods" S. Ghoshray "Heuristic Techniques in Tax Structuring for Multinationals" D. Fatouros, G. Salkin, N. Christofides "MLP and Fuzzy Approaches to Prediction of the SEC's Investigative Targets" E. Feroz, T. Kwon "A Corporate Solvency Map Through Self-Organizing Neural Networks" Y. Alici "The Applicability of Information Criteria for Neural Network Architecture Selection" C. Haefke, C. Helmenstein "Stock Prediction Using Different Neural Network Classification Architectures" C. Dagli, K. Schierholt -- Payman Arabshahi Electronic Publicity Chair, CIFEr'96 Tel : (205) 895-6380 Dept. of Electrical & Computer Eng. Fax : (205) 895-6803 University of Alabama in Huntsville payman@ebs330.eb.uah.edu Huntsville, AL 35899 http://www.eb.uah.edu/ece From glinert@cs.rpi.edu Mon Jan 22 09:01:21 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 08:58:48 -0600; AA00846 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sun, 21 Jan 96 12:01:59 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa15527; 19 Jan 96 10:27:21 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa15525; 19 Jan 96 10:07:42 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa21702; 19 Jan 96 10:06:43 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa20777; 18 Jan 96 22:17:54 EST Received: from cs.rpi.edu by CS.CMU.EDU id aa04132; 18 Jan 96 22:17:26 EST Received: from colossus.cs.rpi.edu by cs.rpi.edu (5.67a/1.4-RPI-CS-Dept) id AA24440; Thu, 18 Jan 1996 22:10:20 -0500 (glinert from colossus.cs.rpi.edu) Date: Thu, 18 Jan 96 22:10:13 EST From: glinert@cs.rpi.edu Received: by colossus.cs.rpi.edu (4.1/2.3-RPI-CS-client) id AA20547; Thu, 18 Jan 96 22:10:13 EST Message-Id: <9601190310.AA20547@colossus.cs.rpi.edu> To: brandj@aol.com, brewer_ju@a1.tch.harvard.edu, brightman@applelink.apple.com, brill@blaze.cs.jhu.edu, britell@u.washington.edu, bryan@blazie.com, bsmall@sfrsa.com, burke@ucla.edu, caragher@tsbbs02.tnet.com, card@parc.xerox.com, cbfb_gwk@selway.umt.edu, ccacnc@aol.com, ccdanj@aol.com, cemayo@tenet.edu, chico@innosys.com, childers@drwho.ee.ufl.edu, chorn@ccmail.unl.edu, clairc@ix.netcom.com, coco@siggraph.org, cole@cse.ogi.edu, colgate@nwu.edu, colibri@let.ruu.nl, collins@enga.bu.edu, comp-phon@cogsci.ed.ac.uk, comp-speech@cs.utexas.edu, connectionists@cs.cmu.edu, corpora@hd.uib.no, craik@beaver.edu, crosby@uhunix.uhcc.hawaii.edu, csax@ucsvax.sdsu.edu, curt@boombox.micro.umn.edu, dati@asel.udel.edu, ddutton@ix.netcom.com, debby@skivs.ski.org Subject: ASSETS'96 AP + Reg Forms /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\ ADVANCE PROGRAM AND REGISTRATION FORMS ASSETS'96 The Second International ACM/SIGCAPH Conference on Assistive Technologies April 11 - 12, 1996 Waterfront Centre Hotel Vancouver BC, Canada Sponsored by the ACM's Special Interest Group on Computers and the Physically Handicapped, ASSETS'96 is the second of a new series of conferences whose goal is to provide a forum where researchers and developers from academia and industry can meet to exchange ideas and report on new developments relating to computer-based systems to help people with impairments and disabilities of all kinds. This announcement includes 4 parts: o Message from the Program Chair o ASSETS'96 Advance Program o ASSETS'96 Registration Form o Hotel Information If you have any questions or would like further information, please consult the conference web pages at http://www.cs.rpi.edu/assets or contact the ASSETS'96 General Chair: Ephraim P. Glinert Dept. of Computer Science R. P. I. Troy, NY 12180 Phone: (518) 276 2657 E-mail: glinert@cs.rpi.edu /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\ MESSAGE FROM THE PROGRAM CHAIR ============================== As Assets '96 Program Chair, I am pleased to extend a warm invitation to you to attend ASSETS'96, the 1996 ACM/SIGCAPH International Conference on Assistive Technologies! This is the second in an annual series of meetings whose goal is to provide a forum where researchers and developers from academia and industry can meet to exchange ideas and report on leading edge developments relating to computer based systems to help people with disabilities. This year, conference attendees will hear 21 exciting presentations on state-of-the art approaches to vision impairments, motor impairments, hearing impairments, augmentative communication, special education needs, Internet access issues, and much more. All submissions have undergone a rigorous review process to assure that the program is of the high technical quality associated with the best ACM conferences, and no more papers have been accepted than can comfortably be presented in a single track (no parallel sessions), with ample time included in the schedule for interaction among presenters and attendees. Come join us in beautiful Vancouver for a great time and a rewarding professional experience! David L. Jaffe VA Palo Alto Health Care System /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\ ASSETS'96 ADVANCE PROGRAM ========================= NOTE: For each paper, only the affiliation of the first author is given. WED 4/10: 6:00 pm - 9:00 pm Registration + Reception THU 4/11: 8:00 am - 5:00 pm Registration 8:00 am - 9:00 am Continental Breakfast 8:45 am - 9:00 am Welcome to ASSETS'96! 9:00 am -10:00 am KEYNOTE ADDRESS: David Rose, Center for Applied Special Technology (CAST) 10:00 am -10:30 am Break 10:30 am -12:00 noon Papers I: The User Interface I "Touching and hearing GUIs: Design issues for the PC access system" C. Ramstein, O. Martial, A. Dufresne, M. Carignan, P. Chasse and P. Mabilleau Center for Information Technologies Innovation (Canada) "Enhancing scanning input with nonspeech sounds" S.A. Brewster, V. Raty and A. Kortkangas University of Glasgow (UK) "A study of input device manipulation difficulties" S. Trewin University of Edinburgh (UK) 12:00 - 1:00 pm Lunch 1:00 pm - 2:00 pm SIGCAPH Business Meeting 2:00 pm - 3:00 pm Papers II: The World Wide Web "V-Lynx: Bringing the World Wide Web to sight-impaired users" M. Krell and D. Cubranic University of Southern Mississippi (USA) "Computer generated 3-dimensional models of manual alphabet shapes for the World Wide Web" S. Geitz, T. Hanson and S. Maher Gallaudet University (USA) 3:00 pm - 3:30 pm Break 3:30 pm - 5:30 pm Papers III: Vision Impairments I "EMACSPEAK: Direct Speech Access" T.V. Raman Adobe Systems "The Pantobraille: Design and pre-evaluation of a single cell braille display based on a force feedback device" C. Ramstein Center for Information Technologies Innovation (Canada) "Interactive tactile display system: A support system for the visually impaired to recognize 3D objects" Y. Kawai and F. Tomita Electrotechnical Laboratory (Japan) "Audiograf: A diagram reader for the blind" A.R. Kennel Institut fur Informationssysteme (Switzerland) 6:00 pm - 9:00 pm Buffet Dinner 8:00 pm - 9:00 pm ASSETS'97 Organizational Meeting FRI 4/12: 8:00 am -12:00 noon Registration 8:00 am - 9:00 am Continental Breakfast 9:00 am -10:00 am Papers IV: Empirical Studies "EVA, an early vocalization analyzer: An empirical validity study of computer categorization" H.J. Fell, L.J. Ferrier, Z. Mooraj, E. Benson and D. Schneider Northeastern University (USA) "An approach to the evaluation of assistive technology" R.D. Stevens and A.D.N. Edwards University of York (UK) 10:00 am -10:30 am Break 10:30 am -12:00 noon Papers V: The User Interface II "Designing interface toolkit with dynamic selectable modality" S. Kawai, H. Aida and T. Saito University of Tokyo (Japan) "Multimodal input for computer access and augmentative communication" A. Smith, J. Dunaway, P. Demasco and D. Peischl A.I. duPont Institute / University of Delaware (USA) "The Keybowl: An ergonomically designed document processing device" P.J. McAlindon, K.M. Stanney and N.C. Silver University of Central Florida (USA) 12:00 - 1:00 pm Lunch 1:00 pm - 2:00 pm Panel Discussion "Designing the World Wide Web for people with disabilities" M.G. Paciello, Digital Equipment Corporation (USA) G.C. Vanderheiden, TRACE R&D Center (USA) L.F. Laux, US West Communications, Inc. (USA) P.R. McNally, University of Hertfordshire (UK) 2:00 pm - 3:00 pm Papers VI: Multimedia "A gesture recognition architecture for sign language" A. Braffort LIMSI/CNRS (France) "`Composibility': Widening participation in music making for people with disabilities via music software and controller solutions" T. Anderson and C. Smith University of York (UK) 3:00 pm - 3:30 pm Break 3:30 pm - 5:30 pm Papers VII: Vision Impairments II "A generic direct manipulation 3D auditory environment for hierarchical navigation in nonvisual interaction" A. Savidis, C. Stephanidis, A. Korte, K. Crispien and K. Fellbaum Foundation for Research and Technology - Hellas (Greece) "Improving the usability of speech-based interfaces for blind users" I.J. Pitt and A.D.N. Edwards University of York (UK) "TDraw: A computer-based tactile drawing tool for blind people" M. Kurze Free University of Berlin (Germany) "Development of dialogue systems for a mobility aid for blind people: Initial design and usability testing" T. Strothotte, S. Fritz, R. Michel, A. Raab, H. Petrie, V. Johnson, L. Reichert and A. Schalt Universitat Magdeburg (Germany) 5:30 pm Closing Remarks /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\ ASSETS'96 REGISTRATION FORM =========================== This form is 2 pages long. Please print it out, complete both pages and mail it WITH FULL PAYMENT to: Ephraim P. Glinert, ASSETS'96 Dept. of Computer Science R. P. I. Troy, NY 12180 We're sorry, but e-mail registration forms and/or forms not accompanied by full payment (check or credit card information) CANNOT be accepted. CONFERENCE REGISTRATION FEES EARLY LATE / ON-SITE -------------------------------------------------------- ACM member: $ 395 $ 475 Nonmember: $ 580 $ 660 Full time student: $ 220 $ 270 -------------------------------------------------------- 1: CONFERENCE REGISTRATION (from the table above): $ ___________ 2: SECOND BUFFER DINNER TICKET (Thursday, April 11): $ 50 ___YES ___NO 3: SECOND COPY OF THE CONFERENCE PROCEEDINGS: $ 30 ___YES ___NO TOTAL AMOUNT DUE: $ ___________ NOTES: o Registration fee includes: ADMISSION to all sessions ONE COPY of the conference PROCEEDINGS RECEPTION, 5 MEALS AND 4 BREAKS as shown in the Advance Program!!! o To qualify for the EARLY RATE, your registration must be postmarked on or before WEDNESDAY, MARCH 27, 1996. If you are an ACM MEMBER, please supply your ID# __________________ . STUDENTS, please attach a clear photocopy of your valid student ID. o CANCELLATIONS will be accepted up to FRIDAY, MARCH 15, 1996 subject to a 20% handling fee. ASSETS'96 REGISTRATION FORM (continued) ======================================= PERSONAL INFORMATION: Name __________________________________________________________________________ Affiliation ___________________________________________________________________ Address _______________________________________________________________________ City _______________________________ State/Province __________________________ Country __________________________________ ZIP/Postal Code ___________________ E-mail ________________________________________________________________________ Phone ___________________________________ FAX ________________________________ ***I have a disability for which I require special accommodation ___YES ___NO If YES, please attach a separate sheet with details. Thank you! PAYMENT INFORMATION: ___CHECK in U.S. funds enclosed, made payable to "ACM ASSETS'96" ___Please charge $ ___________ to my CREDIT CARD: Card type: ___AMEX ___VISA ___MasterCard Card # _______________________________________ Expiration Date ___________ Name On Card ______________________________________________________________ Billing Address ___________________________________________________________ Cardholder Signature ________________________________________ (ASSETS'96) /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\ HOTEL INFORMATION ================= All conference events will take place at the Waterfront Centre Hotel, a member of the Canadian Pacific group. The hotel is located in downtown Vancouver, next to the convention center and cruise ship terminal. Waterfront Centre Hotel 900 Canada Place Way Vancouver, British Columbia V6C 3L5 CANADA Phone: (604) 691 1991 or (800) 441 1414 FAX: (604) 691 1999 A block of rooms for attendees of ASSETS'96 has been set aside at specially discounted rates: Single $140 Canadian per night, plus applicable taxes Double/Twin $160 Canadian per night, plus applicable taxes Waterfront Suite $360 Canadian per night, plus applicable taxes To reserve space at these prices, please call the hotel directly on or before MARCH 15, 1996 and refer to "ACM ASSETS'96". /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\ If you have any questions or would like further information, please consult the conference web pages at http://www.cs.rpi.edu/assets or contact the ASSETS'96 General Chair: Ephraim P. Glinert Dept. of Computer Science R. P. I. Troy, NY 12180 Phone: (518) 276 2657 E-mail: glinert@cs.rpi.edu /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\ From pazzani@super-pan.ICS.UCI.EDU Mon Jan 22 09:04:14 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 09:04:01 -0600; AA01113 Received: from paris.ics.uci.edu by lucy.cs.wisc.edu; Sun, 21 Jan 96 18:42:31 -0600 Received: from super-pan.ics.uci.edu by paris.ics.uci.edu id aa24866; 21 Jan 96 14:12 PST To: ML-LIST:; Subject: Machine Learning List: Vol. 8, No. 1 Reply-To: ml@ics.uci.edu Date: Sun, 21 Jan 1996 13:28:53 -0800 From: Michael Pazzani Message-Id: <9601211412.aa24866@paris.ics.uci.edu> Machine Learning List: Vol. 8, No. 1 Sunday, January 21 1996 Contents: Index of WWW Pages for Machine Learning Courses Univ. GA Course Information Book Book of 34 Student Papers Press Release on ML-related work at JPL book announcement Postdoc opening Dissertation announcement Research post in Glasgow hard to find ML alg. Announcing: new version of Mobal available special issue of Machine Learning New Journal -- Data Mining and Knowledge Discovery CFP: WCNN '96 Session on Evolutionary/Genetic/Annealing Algorithms TARK VI Call for Participation CFP: ECAI96 WS Intelligent Data Analysis in Medicine and Pharmacology EP96 Conference Announcement CFP: AAAI-96 WS on Intelligent Adaptive Agents CFP: Relevance in Knowledge Representation and Reasoning CFP: TAINN'96, Conf on AI & NN (Istanbul/Turkey) The Machine Learning List is moderated. Contributions should be relevant to the scientific study of machine learning. Mail contributions to ml@ics.uci.edu. Mail requests to be added or deleted to ml-request@ics.uci.edu. Back issues may be FTP'd from ics.uci.edu in pub/ml-list/V/ or N.Z where X and N are the volume and number of the issue; ID: anonymous PASSWORD: URL- http://www.ics.uci.edu/AI/ML/Machine-Learning.html ---------------------------------------------------------------------- Date: Sun, 24 Dec 1995 16:09:38 CST From: Vasant Honavar Subject: Index of WWW Pages for Machine Learning Courses A partial list of WWW pages for graduate and undergraduate courses on machine learning and closely related topics can be found at http://www.cs.iastate.edu/~honavar/Courses/cs673/machine-learning-courses.html If you have set up a WWW page that includes machine learning course materials (assignments, software, lecture notes, syllabus, etc.), please email me the relevant information. Thanks. Regards, Vasant Honavar honavar@cs.iastate.edu http://www.cs.iastate.edu/~honavar/homepage.html ------------------------------ Date: Wed, 27 Dec 1995 19:49:44 -0800 (PST) From: "John R. Koza" Subject: Univ. GA Course Information Book NOW AVAILABLE!!! "THE GA 30" Information for Instructors and Prospective Instructors of University Courses on Genetic Algorithms TITLE: "University Courses on Genetic Algorithms 1995 Edition No. 1 - December, 1995 Compiled by John R. Koza, Computer Science Department, Stanford University This volume contains lightly-edited information about 30 different university courses on genetic algorithms that are offered by universities around the world. The information was contributed by the instructors of the various courses. This information was solicited by posting "requests for information" during 1995 on electronic mailing lists on genetic algorithms, genetic programming, and other topics related to evolutionary computation. It is hoped this collection will be useful to both instructors of existing courses on genetic algorithms and instructors considering starting up their own course on this subject. Copies of this volume (ISBN 0-18-195903P8) are available DIRECTLY from Stanford University Bookstore for $9.30 plus $6.00 shipping and handling (in the USA) by calling 415-329-1217 or 800-533-2670 or by writing Stanford Bookstore Stanford University Stanford, California 94305-3079 USA The E-Mail address of the bookstore for e-mail orders is mailorder@bookstore.stanford.edu. Be sure to mention the ISBN number, exact title, refer to "Custom Publishing" and "CSD 000" when ordering to avoid confusion with course readers, collections of student papers, and other materials associated with my courses at Stanford. John R. Koza Consulting Professor Computer Science Department Gates Building Stanford University Stanford, California 94305 USA PHONE: 415-941-0336 E-MAIL: Koza@Cs.Stanford.Edu WWW: http://www-cs-faculty.stanford.edu/~koza/ ------------------------------ Date: Wed, 27 Dec 1995 20:11:27 -0800 (PST) From: "John R. Koza" Subject: Book of 34 Student Papers NOW AVAILABLE!!! A new collection of 34 student papers on GA and GP "Genetic Algorithms and Genetic Programming at Stanford 1995" Compiled by John R. Koza, Computer Science Department, Stanford University This volume (ISBN 0-18-195720-5) contains 34 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer Science 426) at Stanford University offered during the fall quarter 1995 (both on campus and on SITN TV). The appendix to this volume contains material providing basic information about the course, including schedules, reading lists, project instructions, and the take-home final. In the take-home final examination in this course, each student "peer reviews" 4 papers written by other students in the class. Copies of the 1995 volume (ISBN 0-18-195720-5) are available DIRECTLY from Stanford University Bookstore for $14.96 plus $6.00 shipping and handling (in the USA) by calling 415-329-1217 or 800-533-2670 or by writing Stanford Bookstore Stanford University Stanford, California 94305-3079 USA The E-Mail address of the bookstore for e-mail orders is mailorder@bookstore.stanford.edu. Be sure to mention the ISBN number, exact title, refer to "Custom Publishing" and "CSD 000" when ordering to avoid confusion with course readers, collections of student papers, and other materials associated with my courses at Stanford. John R. Koza Consulting Professor Computer Science Department Gates Building Stanford University Stanford, California 94305 USA PHONE: 415-941-0336 E-MAIL: Koza@Cs.Stanford.Edu WWW: http://www-cs-faculty.stanford.edu/~koza/ ------------------------------------------------------------- TABLE OF CONTENTS Evolving Efficient Algorithms by Genetic Programming: A Case Study in Sorting by Eric T. Bauer Using Genetic Algorithms and Convolution to Find Optimal Strategies in Games without Perfect Information by Joey Beheler Genetic Fitting: Evolutionary Search of Optimal Approximations for Discrete Functions by Luca Benini Location Independent Pattern Recognition using Genetic Programming by Markus M. Breunig Valid English Word Classifier Using Genetic Programming by King Choi Chan Optimizing Local Area Networks Using Genetic Algorithms by Andy Choi Predator-Prey Interactions in a Simulated World by Adam Clark Evolution of General Algorithmic Solutions for Simple Sliding Tile Puzzles by Thomas Dillon Evolving Effective Solutions in Effective Amounts of Time by David Engel The Application of Genetic Programming to Cooperative Movement Planning and Execution by John Hart Genetic Programming of Near Minimum Time Spacecraft Attitude Maneuvers by Brian Howley A Genetic Algorithm for a Stochastic Network Planning Problem by David Joffe An Attempt to Evolve Cooperation Among Separately Evolved Structure in Genetic Programming by Bryan H. Johnson Error Driven Parallelization of a Genetic Program by Sesha Kalyur Behavior Learning and Individual Cooperation in Autonomous Agents as a Result of Interaction Dynamics with the Environment by Sejal Kamani The Genetically Determined Dream Team by Mark Kanok A Variable Complexity Genetic Algorithm for Job Allocation by Sanjay Kapoor Using Genetic Algorithm and Decision Trees to produce a Hybrid Classification System by D'ondria L. Kennard Development of Navigational Controllers for Vehicles in Highway Traffic Situations via Genetic Programming by Lisa A. Laane Camera Placement for Optimal Visibility by Vui Chiap Lam The Genetic Algorithm applied to Gate Sizing by Jeremy R. Levitt An Evolutionary Approach to CPU Fault Isolation by Keith Mac Donald Emergent Behavior in Traffic Light Controllers using Genetic Programming by Ari W. Mozes The Hannibal Project by Carl Orthlieb On the Use of Genetic Programming in Elevator Control Design by Dan Pietrasik Evolution of Communication and Division of Labor via Genetic Programming by Hanno Sander Genetic Algorithms Applied to Machine Language by Christian R. Shelton An Empirical Comparison of 3 Population-Based Search Algorithms for the Traveling Salesman Problem by Sanjeev Singh Discovering Patterns in Two-Dimensional Cellular Automata by Caz Taylor Are Your Ready for Some Football? Genetically Produced Ratings for College Football Teams by Howard Thompson Recognition and Reconstruction of Visibility Graphs Using a Genetic Algorithm by Marshall S. Veach Genetic Evolution of Behavior-Oriented Robots by Thomas Willeke Playing Tetris Using Genetic Programming by Michael Yurovitsky Genetic Algorithms in the Solution of Assembly Line Balancing Problems by Greg Zaric Appendix containing materials about the course ------------------------------------------------------------- ALSO AVAILABLE Contact the bookstore directly for current prices on these past items: --- Genetic Algorithms at Stanford 1994 (ISBN 0-18- 187263-3) P 20 papers from the fall quarter 1994. --- Artificial Life at Stanford 1994 (ISBN 0-18-182105-2) P 22 papers from the spring quarter 1994. --- Artificial Life at Stanford 1993 (ISBN 0-18-171957-6) --- Genetic Algorithms at Stanford 1993 (ISBN 0-18- 1738252). --- A course reader entitled Course Reader for Computer Science 426 (Genetic Algorithms) for Fall Quarter 1995 (ISBN 0-18-192183-9) contains 10 selected papers from the current genetic algorithms and genetic programming literature to supplement the two textbooks used in the CS 426 course. ------------------------------ Date: Thu, 28 Dec 95 16:34:22 PST From: Usama Fayyad Subject: Press Release on ML-related work at JPL News Release California Institute of Technology Office of Media Relations Pasadena, CA 91106 (818) 395-3227 For Immediate Release December 1, 1995 Astronomers Announce Discovery of Extremely Distant Quasars PASADENA--Astronomers have discovered 16 new extremely distant quasars, the result of a search made nearly 40 times more efficient than previously possible by applying artificial intelligence to the new Palomar digital sky survey. This novel technique allows researchers to study more easily the formation of quasars and large-scale structures in the early universe. "This is one of the first successful major applications of artificial intelligence techniques in astronomy and space science," said Usama Fayyad, a scientist at the Jet Propulsion Laboratory (JPL) in Pasadena, California. "Data mining techniques and automated data analysis are becoming a necessity in this new era of astronomy and space science where instruments can generate tremendous amounts of data. The discovery of these new quasars shows how efficiently scientists can explore vast databases such as the Palomar sky survey, using this novel data-mining technology. And this technique is applicable to many other data-rich fields. It is a truly new way of doing science." These results are reported in the December issue of the Astronomical Journal in a paper by Julia Kennefick, a postdoctoral researcher at Ohio State University; S. George Djorgovski, an assistant professor of astronomy at Caltech; and Reinaldo Ramos de Carvalho, a senior research fellow in astronomy, also at Caltech. Kennefick is a former graduate student of Djorgovski. Some of the technical developments leading to these discoveries have been reported earlier by Djorgovski, Fayyad, and their colleagues. The astronomers have found 16 new quasars at redshifts greater than 4 (redshift is a measure of distance in cosmology), corresponding to look-back times in excess of 90 percent of the age of the universe. Such objects are exceedingly rare, and finding even a few of them is considered very important by astronomers. The recently discovered quasars are providing a new glimpse of the very early universe. "We see these quasars at a time when the universe was only a billion years old, when the first structures were just forming," explained Djorgovski. The study in the Astronomical Journal confirms a previous suggestion that the number of quasars diminishes rapidly as one looks back toward earlier epochs in the universe. In other words, astronomers are seeing the appearance of the first quasars, when the universe was only one-tenth of its present age, or possibly even younger. The scientists, led by Djorgovski, are conducting a systematic search to discover large numbers of extremely distant quasars using a set of sophisticated artificial intelligence (AI) software tools developed for this task in collaboration with Fayyad and his Machine Learning Systems Group at JPL. The astronomers are applying these AI tools to a new digital survey of the entire northern sky. The digital sky survey is being produced as a collaborative project between Caltech and the Space Telescope Science Institute in Baltimore, Maryland, and is based on the photographic sky survey done with the 48-inch Oschin Telescope, a Schmidt telescope at Caltech's Palomar Observatory in northern San Diego County. When complete, the digital sky survey will contain enough information to fill about 6 million books and will include about 2 billion stars, galaxies, quasars, and other objects. In order to efficiently process this unprecedented amount of astronomical information, a team of scientists from JPL led by Fayyad, in collaboration with Djorgovski and his former student Nicholas Weir, now at Goldman, Sachs and Company in New York, developed a powerful software system, called the Sky Image Cataloging and Analysis Tool (SKICAT). The SKICAT system incorporates cutting-edge AI technology, including machine learning, machine-assisted discovery, and a high-performance database system to automatically measure and classify the billions of objects in the sky survey images, and to assist astronomers in performing scientific analyses of the resulting catalogs. The Caltech group used SKICAT to select quasar candidates from catalogs of objects detected in the sky survey, sorting through roughly one million other objects to find each quasar. On photographs, quasars are indistinguishable from ordinary stars in our galaxy. "This is far more difficult than finding needles in a haystack," said Kennefick. "SKICAT allows us to automatically sort through and pinpoint interesting quasar candidates based on their color, so that we can make the best possible use of the valuable telescope time in checking them out." A previous survey for quasars at comparable distances done at Palomar used about 20 times more nights, with the 200-inch Hale Telescope, and found only nine quasars. "This great increase in the observing efficiency is due to a combination of the huge amount of data in the sky survey, and the modern software techniques that allow us to explore it," Djorgovski said. "And the more quasars we find, the better we will be able to map these early epochs of the universe." "Data mining and automated analysis of large databases offer the promise of giving us a handle on the data avalanche generated by NASA instruments on missions to planet earth and elsewhere in the solar system," said Mel Montemerlo, the manager of the Autonomy and Operations Program at NASA headquarters in Washington, DC. This work was supported by the National Aeronautics and Space Administration, with additional funding from the National Science Foundation. # # # Technical contacts: at JPL: Dr. Usama Fayyad fayyad@aig.jpl.nasa.gov Machine Learning Systems Group Jet Propulsion Lab 525-3660 California Institute of Technology Pasadena, CA 91109 U.S.A. at Caltech: Prof. George Djorgovski george@deimos.caltech.edu Astronomy/Palomar Observatory California Institute of Technology Pasadena, CA 91125 U.S.A. ------------------------------ Date: Fri, 29 Dec 95 15:37:35 MST From: Melanie Mitchell Subject: book announcement Announcing a new book: An Introduction to Genetic Algorithms by Melanie Mitchell MIT Press Complex Adaptive Systems series. A Bradford Book Available January 1996 ISBN 0-262-13316-4 232 pp. $30.00 >From the book jacket: Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics --- particularly in machine learning, scientific modeling, and artificial life --- and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation. Melanie Mitchell is Research Professor and Director of the Adaptive Computation Program at the Santa Fe Institute. Complex Adaptive Systems series. A Bradford Book ------------------------------------------------------------- Table of contents: Chapter 1: Genetic Algorithms: An Overview A Brief History of Evolutionary Computation The Appeal of Evolution Biological Terminology Search Spaces and Fitness Landscapes Elements of Genetic Algorithms A Simple Genetic Algorithm Genetic Algorithms and Traditional Search Methods Some Applications of Genetic Algorithms Two Brief Examples How do Genetic Algorithms Work? Thought Exercises Computer Exercises Chapter 2: Genetic Algorithms in Problem-Solving Evolving Computer Programs Data Analysis and Prediction Evolving Neural Networks Thought Exercises Computer Exercises Chapter 3: Genetic Algorithms in Scientific Models Modeling Interactions Between Learning and Evolution Modeling Sexual Selection Modeling Ecosystems Measuring Evolutionary Activity Thought Exercises Computer Exercises Chapter 4: Theoretical Foundations of Genetic Algorithms Schemas and the Two-Armed Bandit Problem Royal Roads Exact Mathematical Models of Simple Genetic Algorithms Statistical Mechanics Approaches Thought Exercises Computer Exercises Chapter 5: Implementing a Genetic Algorithm Introduction When Should a Genetic Algorithm Be Used? Encoding a Problem for a Genetic Algorithm Adapting the Encoding Selection Methods Genetic Operators Parameters for Genetic Algorithms Thought Exercises Computer Exercises Chapter 6: Conclusions and Future Directions Appendices Selected General References Other Resources Bibliography ------------------------------------------------------------- For more information, see http://www-mitpress.mit.edu/mitp/recent-books/cog/mitnh.html Ordering via WWW: http://www-mitpress.mit.edu/ Orders via email: mitpress-orders@mit.edu Toll Free: 1-800-356-0343 Orders and Book Information: (617) 625-8569 Fax: (617) 625-6660 Snail mail: The MIT Press 55 Hayward Street Cambridge, MA 02142-1399 ------------------------------ Date: Fri, 29 Dec 95 17:55:16 MST From: dhw@santafe.edu Subject: Postdoc opening The Santa Fe Institute is soliciting applications for a TXN postdoctoral fellow. The fellow is expected to perform research in Machine Learning, Artificial Intelligence, or related areas of statistics. Information about the SFI can be found at http://www.santafe.edu/. Candidates should have a Ph.D. (or expect to receive one soon) and should have backgrounds in computer science, mathematics, statistics, or related fields. Applicants should submit a curriculum vitae, list of publications, statement of research interests, and three letters of recommendation. Please submit your materials in one complete package. Incomplete applications will not be considered. All application materials must be received by March 1, 1996. Decisions will be made by April, 1996. Send complete application packages only, preferably hard copy, to: TXN Postdoctoral Committee Attention: David Wolpert Santa Fe Institute 1399 Hyde Park Road Santa Fe, New Mexico 87501 Include your e-mail address and/or fax number. The SFI is an equal opportunity employer. Women and minorities are encouraged to apply. ------------------------------ Date: Fri, 5 Jan 1996 17:41:54 -0800 From: ali@almaden.ibm.com Subject: Dissertation announcement The following dissertation is available via anonymous FTP and through http://www.ics.uci.edu/~ali (either as a whole or by chapters). Title: "Learning Probabilistic Relational Concept Descriptions" By Kamal Ali Key words: Learning probabilistic concepts, multiple models, multiple classifiers, combining classifiers, evidence combination, relational learning, First-order learning, Noise-tolerant learning, Learning of small disjuncts, Inductive Logic Programming. A B S T R A C T This dissertation presents results in the area of multiple models (multiple classifiers), learning probabilistic relational (first order) rules from noisy, "real-world" data and reducing the small disjuncts problem - the problem whereby learned rules that cover few training examples have high error rates on test data. Several results are presented in the arena of multiple models. The multiple models approach in relevant to the problem of making accurate classifications in ``real-world'' domains since it facilitates evidence combination which is needed to accurately learn on such domains. It is also useful when learning from small training data samples in which many models appear to be equally "good" w.r.t. the given evaluation metric. Such models often have quite varying error rates on test data so in such situations, the single model method has problems. Increasing search only partly addresses this problem whereas the multiple models approach has the potential to be much more useful. The most important result of the multiple models research is that the *amount* of error reduction afforded by the multiple models approach is linearly correlated with the degree to which the individual models make errors in an uncorrelated manner. This work is the first to model the degree of error reduction due to the use of multiple models. It is also shown that it is possible to learn models that make less correlated errors in domains in which there are many ties in the search evaluation metric during learning. The third major result of the research on multiple models is the realization that models should be learned that make errors in a negatively-correlated manner rather than those that make errors in an uncorrelated (statistically independent) manner. The thesis also presents results on learning probabilistic first-order rules from relational data. It is shown that learning a class description for each class in the data - the one-per-class approach - and attaching probabilistic estimates to the learned rules allows accurate classifications to be made on real-world data sets. The thesis presents the system HYDRA which implements this approach. It is shown that the resulting classifications are often more accurate than those made by three existing methods for learning from noisy, relational data. Furthermore, the learned rules are relational and so are more expressive than the attribute-value rules learned by most induction systems. Finally, results are presented on the small-disjuncts problem in which rules that apply to rare subclasses have high error rates The thesis presents the first approach that is simultaneously successful at reducing the error rates of small disjucnts while also reducing the overall error rate by a statistically significant margin. The previous approach which aimed to reduce small disjunct error rates only did so at the expense of increasing the error rates of large disjuncts. It is shown that the one-per-class approach reduces error rates for such rare rules while not sacrificing the error rates of the other rules. The dissertation is approximately 180 pages long (single spaced) (~590K). ftp ftp.ics.uci.edu logname: anonymous password: your email address cd /pub/ali binary get thesis.ps.Z quit ============================================================================ I am now with the IBM Data Mining group at Almaden (San Jose) - we are looking for good people for data analysis (data mining) and consulting so please feel free to call me at (408) 365 8736. My address is: Kamal Ali, Room D3-250 IBM Almaden Research Center 650 Harry Rd San Jose, CA 95120 ============================================================================== Kamal Mahmood Ali, Ph.D. Phone: 408 927 1354 Consultant and data mining analyst, Fax: 408 927 3025 Data Mining Solutions, Office: ARC D3-250 IBM http://www.almaden.ibm.com/stss/ ============================================================================== ------------------------------ Date: Fri, 12 Jan 96 16:44:11 GMT From: "J.Cussens" Subject: Research post in Glasgow DEPARTMENT OF MATHEMATICS GLASGOW CALEDONIAN UNIVERSITY RESEARCH FELLOW IN MACHINE LEARNING Applications are invited from suitably qualified or experienced persons for the position of Research Fellow in Machine Learning. The post is located in the Department of Mathematics and is to support existing programmes of research into inductive rule learning for flight critical and aerospace related applications. An appointment will be made at an appropriate point on the scale GBP 16628 - GBP 21519 and will be for one year in the first instance. Further information may be obtained from: Professor Roy Bradley Department of Mathematics Glasgow Caledonian University Glasgow G4 0BA Scotland, UK The closing date for applications is 16th February 1996. Telephone: +44 (0)141 331 3610 FAX: +44 (0)141 331 3608 email: r.bradley@gcal.ac.uk ------------------------------ Date: Mon, 15 Jan 1996 04:50:42 -0600 From: "Douglas H. Fisher" Subject: hard to find ML alg. In research for an IEEE TSE article on estimating software development effort, I came across an article on the Optimized Set Reduction (OSR) algorithm in IEEE TSE (Vol. 18, Nov. 1992). The authors developed this system for estimating software development effort from a training set of completed software projects, but the ideas are more general. OSR retains a `training' set of observations, each represented by attribute-value pairs. When a test observation is presented, OSR searches for conjunctive rules, supported by the `training' data, that best match the observation, and makes a prediction by combining evidence from the various rules that are discovered. It does not add discovered rules to a `compiled' set of rules, but conducts a new search for each test observation. OSR's search might be viewed as a `beam' search that is driven by an information measure. There are some interesting relationships between OSR and DL learners, particularly a system like BruteDL. Looking at these systems together seems to make two issues clear: (1) the possibility of using evidence combination in a DL system, rather than simply an degenerate form of evidence combination -- i.e., conflict resolution, and (2) the relationship between macro learning in problem solving contexts and conjunctive rule learning in DL systems -- conjunctive rules discovered during concept learning are macro operators, though quite impoverished given an attribute-value representation. A Masters student recently completed a thesis in which OSR was used as a default `strategy' (in place of a default rule that is more standard in DLs) in conjunction with BruteDL -- some interesting, though modest results. In any case, OSR is an interesting algorithm that suggests some lines of work and that you might not have otherwise come across. Id be interested in refs. to any work along the `rules as macros' theme. Cheers, Doug Fisher ------------------------------ Date: Thu, 18 Jan 96 19:17:42 +0100 From: Edgar Sommer Subject: Announcing: new version of Mobal available Mobal The knowledge acquisition and machine learning system MOBAL (release 4.1b9) is available free for non-commercial academic use from the anonymous ftp-server 'ftp.gmd.de' in the directory 'gmd/mlt/Mobal'. The system requires a Sun Sparc Station, SunOS 4.1 and X11R5 or later. The user interface is implemented with Tcl/Tk --- all you need to run Mobal is a Sun and X11. The information included below, plus a little more, is more colorfully available at: http://nathan.gmd.de/projects/ml/mobal.html About Mobal 4.1b9 Mobal is a sophisticated system for developing operational models of application domains in a relational knowledge representation. It integrates a manual knowledge acquisition and inspection environment, a powerful inference engine, machine learning methods for automated knowledge acquisition, a knowledge revision tool, and -- this is the new bit -- a host of services related to the topic of Theory Restructuring: * various forms of redundancy analysis & elimination * methods & strategies for changing inferential structure (folding & unfolding) * evaluation criteria for comparing empirically equivalent but syntactically different forms of a theory (such as those produced by applying one or more restructuring operators) * miscellaneous analysis, restructuring and cleanup services: o detailed overview over form & content of the theory (pos, neg, covered, uncovered instances; statistics on the number of rules, predicates, facts, sorts, integrity constraints, metarules & -facts, etc.; focus-able text and graphical views of rules, facts, predicates, etc.) o hypothetical reasoning, esp. explanation of failure to cover o detection of non-generative rules & suggested fix o detection of unused predicates o determination of minimum required inputs relative to a given goal concept See also http://nathan.gmd.de/persons/edgar.sommer/scientific.html By using Mobal's knowledge acquisition environment, you can incrementally develop a model of your domain in terms of logical facts and rules. You can inspect the knowledge you have entered in text or graphics windows, augment the knowledge, or change it at any time. The built-in inference engine can immediately execute the rules you have entered to show you the consequences of your inputs, or answer queries about the current knowledge. Mobal also builds a dynamic sort taxonomy from your inputs. If you wish, you can use machine learning methods to automatically discover additional rules based on the facts that you have entered, or to form new concepts. (Mobal can be used as a front-end for any induction algo that runs under SunOS & can do i/o via files -- if you have one we don't, consult the example interfaces that come with the distribution (in the tools dir), modify to your needs, and send us a message.) If there are contradictions in the knowledge base due to incorrect rules or facts, there is a knowledge revision tool to help you locate the problem and fix it. User Guide MOBAL's User Guide has completely reworked and extended for the new release. A draft of this version is browse-able: http://nathan.gmd.de/projects/ml/mobal.html ... and is part of the distribution package, so if you're getting that, you do not need to get the user guide separately. Acknowledgments Mobal is a result of research funded in part by the European Community within the type B ESPRIT Project 2154 "Machine Learning Toolbox" and the ESPRIT Project "Inductive Logic Programming" (ILP, PE 6020) and is based on the System BLIP developed in the project "Lerner" at the Technical University Berlin funded by the German government (BMFT) under contract ITW8501B1, and is further made possible by the existence of coffee. Thanks! Restrictions MOBAL is available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of FITNESS FOR A PARTICULAR PURPOSE. MOBAL can be used free of charge for academic, educational, or non-commercial uses. We do emphatically request, however, that you send us mail (mobal@gmd.de) so we know where MOBAL is going. Warm words in closing Sadly, work on Mobal is not currently at the apex of our official duties, so we may be slow in responding to Stupid Questions(sm), but promise never to get angry. Please, however, consider RTFM'ing (see pointers to animals called "user guide" & "web pages" above) before attempting to grab our attention with -- examples picked at random -- requests for porting several MB's worth of code to DOS/286 or ZX Spectrum. We are currently fiddling around with an unmoderated mailing list for Mobalites; if you use Mobal, consider sending mail to majordomo@gmd.de, with content subscribe mobal-list Let us know if you are doing anything interesting with Mobal! Cheers, the MLGroup@GMD ---------------------------------------------------------------------------- ML Group http://nathan.gmd.de/projects/ml/home.html GMD (German National Research Center for Information Technology) AI Research Division (FIT.KI) Schloss Birlinghoven D-53754 Sankt Augustin Germany Fax: +49/2241/14-2889 E-Mail: mobal@gmd.de ------------------------------ Date: Mon, 15 Jan 1996 23:18:59 -0800 From: Pat Langley Subject: special issue of Machine Learning Special Issue on Learning with Probabilistic Representations Guest editors: Pat Langley (ISLE/Stanford University) Gregory Provan (Rockwell Science Center/ISLE) Padhraic Smyth (JPL/University of California, Irvine) In recent years, probabilistic formalisms for representing knowledge and inference techniques for using such knowledge have come to play an important role in artificial intelligence. The further development of algorithms for inducing such probabilistic knowledge from experience has resulted in novel approaches to machine learning. To increase awareness of such probabilistic methods, including their relation to each other and to other induction techniques, Machine Learning will publish a special issue on this topic. We encourage submission of papers that address all aspects of learning with probabilistic representations, including but not limited to: Bayesian networks, probabilistic concept hierarchies, naive Bayesian classifiers, mixture models, (hidden) Markov models, and stochastic context-free grammars. We consider any work on learning over representations with explicit probabilistic semantics to fall within the scope of this issue. Submissions should describe clearly the learning task, the representation of data and learned knowledge, the performance element that uses this knowledge, and the induction algorithm itself. Moreover, we encourage authors to decompose their characterization of learning into the separate processes of estimating parameters and search through the space of structures. Good papers present these components independently, whereas poor papers implicitly mix them. Papers should also evaluate the proposed methods using techniques acknowledged in the machine learning literature, including but not limited to: experimental studies of algorithm behavior on natural and synthetic data (but not the latter alone), theoretical analyses of algorithm behavior, ability to model psychological phenomena, and evidence of successful application in real-world contexts. We especially encourage comparisons that clarify relations among different probabilistic methods or to nonprobabilistic techniques. Papers should meet the standard submission requirements given in the Machine Learning instructions to authors, including having length between 8,000 and 12,000 words. Hard copies of each submission should be mailed to: Karen Cullen (5 copies) Pat Langley (1 copy) Kluwer Academic Publishers Institute for the Study 101 Philip Drive of Learning and Expertise Assinippi Park 2164 Staunton Court Norwell, MA 02061 Palo Alto, CA 94306 by the submission deadline, July 1, 1996. The review process will take into account the usual criteria, including clarity of presentation, originality of the contribution, and quality of evaluation. We encourage potential authors to contact Pat Langley (langley@cs.stanford.edu), Gregory Provan (provan@jupiter.risc.rockwell.com), or Padhraic Smyth (pjs@aig.jpl.nasa.gov) prior to submission if they have questions. ------------------------------ Date: Mon, 15 Jan 96 13:32:05 PST From: Data Mining Journal Subject: New Journal -- Data Mining and Knowledge Discovery New Journal Announcement: Data Mining and Knowledge Discovery an international journal http://www.research.microsoft.com/research/datamine/ Published by Kluwer Academic Publishers C a l l f o r P a p e r s Advances in data gathering, storage, and distribution technologies have far outpaced computational advances in techniques for analyzing and understanding data. This created an urgent need for a new generation of tools and techniques for automated Data Mining and Knowledge Discovery in Databases (KDD). KDD is a broad area that integrates methods from several fields including statistics, databases, AI, machine learning, pattern recognition, machine discovery, uncertainty modeling, data visualization, high performance computing, management information systems (MIS), and knowledge-based systems. KDD refers to a multi-step process that can be highly interactive and iterative. It includes data selection/sampling, preprocessing and transformation for subsequent steps. Data mining algorithms are then used to discover patterns, clusters and models from data. These patterns and hypotheses are then rendered in operational forms that are easy for people to visualize and understand. Data mining is a step in the overall KDD process. However, most published work has focused solely on (semi-)automated data mining methods. By including data mining explicitly in the name of the journal, we hope to emphasize its role, and build bridges to communities working solely on data mining. Our goal is to make Data Mining and Knowledge Discovery a flagship journal publication in the KDD area, providing a unified forum for the KDD research community, whose publications are currently scattered among many different journals. The journal will publish state-of-the-art papers in both the research and practice of KDD, surveys of important techniques from related fields, and application papers of general interest. In addition, there will be a pragmatic section including short application reports (1-3 pages), book and system reviews, and relevant product announcements. Please visit the journal's WWW homepage at: http://www.research.microsoft.com/research/datamine/ to obtain further information, including: - A list of topics of interest, - full call for papers, - instructions for submission, - contact information, subscription information, and - ordering a free sample issue. Editors-in-Chief: Usama M. Fayyad ================ Jet Propulsion Laboratory, California Institute of Technology, USA Heikki Mannila University of Helsinki, Finland Gregory Piatetsky-Shapiro GTE Laboratories, USA Editorial Board: =============== Rakesh Agrawal (IBM Almaden Research Center, USA) Tej Anand (AT&T Global Information Solutions, USA) Ron Brachman (AT&T Bell Laboratories, USA) Wray Buntine (Thinkbank Inc, USA) Peter Cheeseman (NASA AMES Research Center, USA) Greg Cooper (University of Pittsburgh, USA) Bruce Croft (University of Mass. Amherst, USA) Dan Druker (Arbor Software, USA) Saso Dzeroski (Jozef Stefan Institute, Slovenia) Oren Etzioni (University of Washington, USA) Jerome Friedman (Stanford University, USA) Brian Gaines (University of Calgary, Canada) Clark Glymour (Carnegie-Mellon University, USA) Jim Gray (Microsoft Research, USA) Georges Grinstein (University of Lowell, USA) Jiawei Han (Simon Fraser University, Canada) David Hand (Open University, UK) Trevor Hastie (Stanford University, USA) David Heckerman (Microsoft Research, USA) Se June Hong (IBM T.J. Watson Research Center, USA) Thomasz Imielinski (Rutgers University, USA) Larry Jackel (AT&T Bell Labs, USA) Larry Kerschberg (George Mason University, USA) Willi Kloesgen (GMD, Germany) Yves Kodratoff (Lab. de Recherche Informatique, France) Pat Langley (ISLE/Stanford University, USA) Tsau Lin (San Jose State University, USA) David Madigan (University of Washington, USA) Ami Motro (George Mason University, USA) Shojiro Nishio (Osaka University, Japan) Judea Pearl (University of California, Los Angeles, USA) Ed Pednault (AT&T Bell Laboratories, USA) Daryl Pregibon (AT&T Bell Laboratories, USA) J. Ross Quinlan (University of Sydney, Australia) Jude Shavlik (University of Wisconsin - Madison, USA) Arno Siebes (CWI, Netherlands) Evangelos Simoudis (IBM Almaden Research Center, USA) Andrzej Skowron (University of Warsaw, Poland) Padhraic Smyth (Jet Propulsion Laboratory, USA) Salvatore Stolfo (Columbia University, USA) Alex Tuzhilin (NYU Stern School, USA) Ramasamy Uthurusamy (General Motors Research Laboratories, USA) Vladimir Vapnik (AT&T Bell Labs, USA) Ronald Yager (Iona College, USA) Xindong Wu (Monash University, Australia) Wojciech Ziarko (University of Regina, Canada) Jan Zytkow (Wichita State University, USA) ====================================================================== If you would like to receive information from Kluwer on this journal, and to receive a free sample issue by mail, please fill out the form attached below, and e-mail it to datamine@aig.jpl.nasa.gov Please use the following in SUBJECT field: REQUEST for SAMPLE J-DMKD ______cut-here______cut-here______cut-here______cut-here______cut-here____ .... Please do NOT remove keywords following '___', simply fill in provided .... fields and return as is. This form will be processed automatically. .... If you do not wish to complete a field, please LEAVE BLANK. .... Subject should be: REQUEST for SAMPLE J-DMK .... mail completed form, including keywords in CAPS to .... datamine@aig.jpl.nasa.gov .... ___ REQUEST FOR FREE SAMPLE ISSUE OF DATA MINING AND KNOWLEDGE DISCOVERY ___ ___ NAME: ___ EMAIL: ___ AFFILIATION: ___ POSTAL_ADDRESS_LINE1: ___ POSTAL_ADDRESS_LINE2: ___ POSTAL_ADDRESS_LINE3: ___ POSTAL_ADDRESS_LINE4: ___ CITY: ___ STATE: ___ ZIP: ___ COUNTRY: ___ TELEPHONE: ___ FAX: ___ END_FORM: do not edit this line, anything below it is discarded. ------------------------------ Date: Sun, 24 Dec 1995 15:39:00 CST From: Vasant Honavar Subject: CFP: WCNN '96 Session on Evolutionary/Genetic/Annealing Algorithms 1996 World Congress on Neural Networks San Diego, CA. September 15-20, 1996. Session on Evolutionary/Genetic/Annealing Algorithms. Session Co-chairs: Judith Dayhoff, University of Maryland Vasant Honavar, Iowa State University Possible topics for paper submissions to this session include (but are not limited to): Evolutionary Synthesis of Neural Systems Evolutionary Design of Intelligent Agents Evolutionary Robotics Evolutionary Design of Neurocontrollers Evolutionary/Neural Hybrid Systems Randomized Search Algorithms for Learning and Optimization Symbiotic Evolution of Neural Architectures Evolution of Language and Communication Genetic Representations of Neural Networks Evolution of Neural Network Learning Algorithms Evolution of Modular Neural Network Architectures Evolutionary Multi-criteria Optimization of Neural Architectures Evolutionary synthesis of Energy-Efficient VLSI Neural Systems Both theoretical as well as experimental papers are welcome. All submissions will be reviewed by anonymous referees. Papers must be received by January 15, 1995 at: WCNN '96, 875 Kings Highway, Suite 200, Woodbury, NJ 08096-3172, U.S.A. Additional information on WCNN '96 is available on the Web via the URL http://sharp.bu.edu/inns/WCNN/96call.html (This message is being sent to multiple mailing lists. My apologies if you receive more than one copy as a result). Regards, Vasant _________________________________________________________ Vasant Honavar Artificial Intelligence Research Group Department of Computer Science 226 Atanasoff Hall Iowa State University Ames, Iowa 50011-1040 email: honavar@cs.iastate.edu www: http://www.cs.iastate.edu/~honavar/homepage.html fax: 515 294 0258 voice: 515 294 1098 ------------------------------ Date: Tue, 9 Jan 1996 14:25:23 +0100 From: Tark Conference Subject: TARK VI Call for Participation Dear reader, please find enclosed a call for registration for TARK VI, the Sixth conference on Theoretical Aspects of Rationality and Knowledge This announcement includes the ascii text of: * TARK VI: description * Conference location * a list of invited speakers, * a program, * a note on tark registration * a registration form. Extend calls are also available: * a brochure can be send to you upon request (ask tark@cs.ruu.nl) * you may inspect and fill in the form at http://www.cs.ruu.nl/docs/tark/ Kind regards, the local TARK-organisers. ==================================================== TARK VI: description. Date: March 17 - 20, 1996. Place: Renesse, Zeeland, The Netherlands The mission of the bi-annual TARK conferences is to bring together researchers from a wide variety of fields - including Artificial Intelligence, Cryptography, Distributed Computing, Economics and Game Theory, Linguistics, Philosophy, and Psychology - in order to further our understanding of interdisciplinary issues involving formal reasoning about rationality and knowledge. Topics of interest include, but are not limited to, semantic models for knowledge, for belief, and for uncertainty, bounded rationality and resource-bounded reasoning, commonsense epistemic reasoning, knowledge and action, applications of reasoning about knowledge and other mental states, and belief revision. Previously a by-invitation-only conference, TARK is now open to all interested attendees. TARK VI is the first to be held outside the United States. It will take place March 17 - 20, 1996. More information about TARK in general, and this conference in particular, is available at WWW: http://www.tark.org http://www.cs.ruu.nl/docs/tark/ ==================================================== Conference location Hotel `De Zeeuwse Stromen' is situated close to the coast of Zeeland. Zeeland is one of the twelve provinces of the Netherlands and it borders Belgium and the Northsea. It is only a three minute walk from the hotel to the beach, which offers great opportunities to stroll across beautiful and serene nature. One of the main attractions of the area is the `Oosterscheldekering' (how do the Dutch control the sea?), but also the old and monumental villages Middelburg, Zierikzee and Veere are worth visiting. The weather in March in the Netherlands is quite unpredictable: there are days with sun, but it can also be rather chilly and wet. Especially at the beach, a pull-over and a warm coat are recommendable. The hotel has lots of facilities: a large lounge with a fireplace, a bar and a lovely winter-garden. The heated indoor-pool is provided with a terrace, a sauna and a solarium. All the rooms have a bathroom, telephone, alarm-clock, colour-t.v. and mini-bar. In the surroundings of the hotel one can find tennis- and mini-golf-courts. Finally, bikes can be rented at the reception-desk to explore the region. Address: Hotel `De Zeeuwse Stromen' Duinwekken 5 Postbus 70 4325 ZG Renesse The Netherlands tel: +31-30 1116-2040 fax: +31-30 1116-2065 ==================================================== Invited Speakers Peter Gardenfors, Cognitive Science (Lund) Belief Revision and Knowledge Representation Ehud Kalai, Economics (Evanston) Rational Interactive Learning in Economics and Game Theory Christos Papadimitriou, Computer Science (Berkeley) Games, Information, and Computational Complexity Judea Pearl, Artificial Intelligence (Los Angeles) Causality, Counterfactuals and Implicit Actions Ariel Rubinstein, Economics (Princeton) Imperfect Recall in Decision Problems Goran Sundholm, Philosophy (Leiden) Constructive Proof Theory and Epistemics Frank Veltman, Logic (Amsterdam) Tutorial on Dynamic Update Semantics ==================================================== TARK Program Sunday, March 17 17.30--19.00 Opening Reception 16.00--20.00 Conference Registration Monday, March 18 8.30--9.00 Conference Registration 9.00--9.10 Welcoming remarks (J. van Benthem, Y. Shoham) 9.15--10.05 Games, Information, and Computational Complexity (C. Papadimitriou, invited talk) 10.10--10.35 Local Knowledge Assertions in a Changing World R. Ramanujam (Inst. of Mathematical Sciences, India) 10.35--11.00 BREAK 11.00--11.50 Special Session: Implementing Knowledge-Based Programs A Review of knowledge-based programs M. Vardi (Rice U., USA) B Implementing Knowledge-Based Programs M. Vardi (Rice U.,USA) C Knowledge-Based Programs: On the Complexity of Perfect Recall in Finite Environments R. van der Meyden (Sydney U. of Technology, Australia) 12.00--14.00 LUNCH 14.00-14.50 Causality, Counterfactuals and Implicit Actions J. Pearl (invited talk) 14.50--15.05 BREAK 15.05--15.55 Imperfect Recall in Decision Problems A. Rubinstein (invited talk) 16.00--16.50 Responses to Rubinstein A Time consistency and Strategy in Games of Imperfect Recall J.Y. Halpern (IBM Almaden, USA) B The Absent-Minded Driver R.J. Aumann, S. Hart, M. Perry (Hebrew U., Israel) 17.00-17.30 Rump Session (attendees encouraged to give short impromptu presentations) 18.30--20.00 DINNER 20.00--20.50 Dynamic Update Semantics (F. Veltman, invited tutorial) Tuesday, March 19 9.00--9.50 Belief Revision and Knowledge Representation P. Gardenfors (invited talk) 9.50--10.05 BREAK 10.05--12.00 Special Session: Belief Change A Changing Conditional Beliefs Unconditionally A. Nayak, N. Foo, M. Pagnucco (U. Sydney, Australia) and A. Sattar (Griffith U., Australia) B Distance Semantics for Belief Revision K. Schlechta, D. Lehmann, M. Magidor (Hebrew U., Israel) C Belief Change and Dependence L. Farrinas del Cerro, A. Herzig (U. Paul Sabatier, France) D Counterfactuals and Updates as Inverse Modalities O. Rodrigues (Imperial College, UK), M.D. Ryan (U. Birmingham, UK) and P-Y. Schobbens (Institut d'Informatique, Belgium) E Multi-Agent Belief Revision N. Kfir-Dahav, M. Tennenholtz (Technion, Israel) 12.00--14.00 LUNCH 14:00--14:50 Rational Interactive Learning in Economics and Game Theory E. Kalai (invited talk) 15.00--15.25 From reinforcement learning to emergent conventions C. Boutilier (U. British Columbia, Canada) 15.25--15.40 BREAK 15.40--16.05 Knowledge at Equilibrium E. Minelli, H.M. Polemarchakis (U. Catholique de Louvain, Belgium) 16.10--16.35 Nondeterministic Action and Dominance:Foundations for Planning and Qualitative Decision R.H. Thomason (U. Pittsburgh, USA) and J.F. Horty (U. Maryland, USA) 16.40--17.30 Rump session (attendees encouraged to give short impromptu presentations) 17.30--19.00 BREAK 19.00--20.30 BANQUET 20.30--21.30 TARK business meeting (all attendees welcome) Wednesday, March 20 9.00--9.50 Constructive Proof Theory and Epistemics G. Sundholm (invited talk) 10.00--10.25 Multi-Agent `Only Knowing' J.Y. Halpern (IBM Almaden, USA) and G. Lakemeyer (U. Bonn, Germany) 10.30--10.55 Rationality Postulates for Induction P.A. Flach (Tilburg U., Netherlands) 10.55--11.10 BREAK 11.10-12.00 Special Session: Common Knowledge Revisited A Computer Science: R. Fagin, J.Y. Halpern (IBM Almaden, USA), Y. Moses (Weizmann Inst., Israel) B Economics and Game Theory: S. Morris (U. Pennsylvania, USA) 12:00--14.00 FAREWELL LUNCH; END OF CONFERENCE 14.00--18.00 optional local excursion ==================================================== TARK registration CONFERENCE AND HOTEL REGISTRATION To register, you may either: Send the registration form to TARK VI, Dept. of Computer Science, Utrecht University, P.O. Box 80.089, 3508 TB Utrecht, the Netherlands. or fax it to +31-30 2513791. or mail it to tark@cs.ruu.nl or use the form at WWW: http://www.cs.ruu.nl/docs/tark/ Please register BEFORE February 9th, otherwise availability of hotel accommodation cannot be guaranteed. The fee includes: Attendance at all TARK sessions Copy of the TARK proceedings Hotel accomodation during the conference Conference-banquet Hotel accomodation is available in single rooms, and includes breakfast, lunch and dinner. PAYMENT Fees are payable in Dutch Guilders (Dfl). Your payment should reach us BEFORE March 10! ==================================================== Registration Form Name: _____________________________________ Department: _____________________________________ Institution: _____________________________________ Address: _____________________________________ Postcode/City: _____________________________________ Country: _____________________________________ Telephone: _____________________________________ E-mail: _____________________________________ Remarks: _____________________________________ In the following, please click what is appropriate: Hotel arrangement from March 17 with departure on March 20 before 17.00 hr: Dfl 750 [ ] Hotel arrangement from March 17 with departure on March 21: Dfl 875 [ ] Do you have special dietary restrictions? Vegetarian [ ] Otherwise ____________________________________ Please check your payment method (unfortunately, we can not accept credit cards): bank transfer (net of bank charges) [ ] into: Postbank account 229847 of Utrecht University Faculteit Wiskunde & Informatica TARK, 0251/1501020 bank cheque payable to TARK, 0251/1501020, send with this registration form. [ ] ------------------------------ Date: Wed, 10 Jan 1996 21:37:42 +0100 (MET) From: Blaz Zupan Subject: CFP: ECAI96 WS Intelligent Data Analysis in Medicine and Pharmacology INTELLIGENT DATA ANALYSIS IN MEDICINE AND PHARMACOLOGY (IDAMAP-96) First Call for Papers for the Workshop at ECAI-96 12th European Conference on Artificial Intelligence August 12-16, 1996 Budapest, Hungary Organized by: Nada Lavrac, J. Stefan Institute, Slovenia (chair) Pedro Barahona, Universidade Nova de Lisboa, Portugal Riccardo Bellazzi, University of Pavia, Italy Werner Horn, Austrian Research Institute for Artificial Intelligence Elpida Keravnou, University of Cyprus (co-chair) Cristiana Larizza, University of Pavia, Italy Blaz Zupan, J. Stefan Institute, Slovenia (co-chair) GENERAL INFORMATION IDAMAP-96, an ECAI-96 workshop, will be held in Budapest, Hungary, on 12 or 13 August 1996, immediately before the main ECAI-96 conference, August 14-16, 1996. The workshop will last one full day. Gathering in an informal setting, workshop participants will have the opportunity to meet and discuss selected technical topics in an atmosphere which fosters the active exchange of ideas among researchers and practitioners. To encourage interaction and a broad exchange of ideas, the workshop will be kept small, preferably around 30 participants. TOPIC The gap between data generation and data comprehension is widening in all fields of human activity. In medicine and pharmacology overcoming this gap is particularly crucial since medical decision making needs to be supported by arguments based on basic medical and pharmacologi- cal knowledge as well as knowledge, regularities and trends extracted from data by intelligent data analysis techniques. The topic of the workshop are computational methods for intelligent data analysis aimed at narrowing the gap between data gathering and data comprehension, as well as their applications in medicine and pharmacology. Topics include, but are not limited to, effective machine learning tools, clustering, data visualization, interpretation of time-ordered data (derivation and revision of temporal trends and other forms of temporal data abstraction), learning with case bases, discovery of new diseases, new drug compounds, pharmacodynamical modelling, predicting drug activity, etc. Emphasis will also be given to solving of problems which result from automated data collection in modern hospitals, such as analysis of computer-based patient records (CPR), analysis of data from patient-data management system (PDMS), intelligent alarming, effective and efficient monitoring, etc. SCIENTIFIC PROGRAM The scientific program of the workshop will consist of presentations of accepted papers and panel discussions. Papers are invited both on methodological issues of data mining as well as on specific applications in medicine and pharmacology. The preferred length of papers is 10 pages. Panel discussions will consist of commentators' views on the presented papers as well as on discussions initialized by participants. In order to be able to organize these discussions, entries for discussions are encouraged on any topic related to the workshop. We especially encourage entries on the topic "Data mining and knowledge discovery - its practical potential in medicine and pharmacology". The preferred length of entries for panel discussions is 1 page. SUBMISSION OF PAPERS Submit papers (preferably 5 hard copies, 8-12 pages, possibly postscript) and panel discussion entries (hardcopy or electronic, 1 page) to: Nada Lavrac, Blaz Zupan J. Stefan Institute Jamova 39 61000 Ljubljana Slovenia tel. +386 61 177 3272, 177 3380 fax. +386 61 125 1038, 219 385 email: ecai96wk@ijs.si Submissions must include first author's complete contact information, including address, email, phone and fax. WORKSHOP PARTICIPATION Workshop participation is not limited to authors of submissions. A limited number of other attendees will be selected based on submitted statements of interest for participation at the workshop. A statement of interest (send an email to ecai96wk@ijs.si) should include the name, address, email, phone, fax and description of research interest. IMPORTANT DATES - Paper submission deadline April 2, 1996 - Notification to Authors April 26, 1996 - Camera-ready papers May 15, 1996 PUBLICATION OF PAPERS Accepted papers will be published in ECAI-96 working notes. It is planned to published a post-conference publication based on selected workshop papers. WORKSHOP FEE - Workshop fee is 50 ECU per participant. - Attendees at workshops must register also for the main ECAI conference. ------------------------------ Date: Wed, 10 Jan 1996 16:43:38 -0500 From: "Peter J. Angeline" Subject: EP96 Conference Announcement The Fifth Annual Conference on Evolutionary Programming (EP96) February 29 to March 2, 1996 The Sheraton Harbor Island Hotel San Diego, CA, USA The EP conference has earned a reputation for being a broad, single session conference covering all Evolutionary Computations and their applications with a special emphasis on Evolutionary Programming. This year's conference has a very strong collection of invited and submitted papers covering topics on the theory, practice and analysis of all forms of evolutionary computations in addition to the application of evolutionary computations to artificial life, economics, biology and biochemistry. Conference Program specifics include: Keynote Speaker: Bernardo Huberman - Xerox PARC "The Dynamics of Multiagent Systems" Banquet Speaker: Bill Schopf- UCLA (Discoverer of the oldest fossil on record!) "Modeling Evolutinary Tempo and Mode: Are the Right Questions Being Asked?" Special Sessions: Evolution and Economic Modeling The High Level Control of Evolutionary Learning Evolutionary Computation in Biology and Biochemistry Submitted Paper Sessions: Theory and Analysis of Evolutionary Computations Self-Adaptive Evolutionary Computations Issues in Evolutionary Optimization Genetic Programming Evolution and Computational Intelligence Learning and Control Applications and Implementation Issues For specific details regarding the content of the various sessions or other conference information, see the Conference WWW page at http://www.owego.com/~pja/ep96.html or contact Peter Angeline at pja@lfs.loral.com. A registration form is included below for your convenience. We encourage everyone to attend what looks to be an exciting EP conference! Pete Angeline Thomas Baeck EP96 Technical Co-Chairs ---------------------------------------------------------------------- EP96 Fifth Annual Conference on Evolutionary Programming Registration Form Prof / Dr / Mr / Ms / Mrs (circle one) Name ________________________________________________________________ Last First MI I would like my name tag to read _____________________________________________ Affiliation/Business ______________________________________________________ Address ______________________________________________________ City ______________________________________________________ State ___________________ Zip ________________________ Country_____________________________________________ Telephone (include area code) Business _______________________________ Home______________________________ Email ____________________________ Fax (include area code) _______________________ FEES (all figures in US dollars) CONFERENCE REGISTRATION FEE On or before January 31, 1996 ___ Regular, $225 ___ Student, $50 =$_________ On or after February 1, 1996 ___ Regular, $275 ___ Student, $90 =$_________ SINGLE DAY REGISTRATION FEES (Does not include proceedings or banquet ticket) Thursday, February 19 ___ Regular, $100 ___ Student, $40 Friday, March 1 ___ Regular, $100 ___ Student, $40 Saturday, March 2 ___ Regular, $100 ___ Student, $40 =$_________ Extra Banquet Tickets (cost of one banquet ticket is included with only CONFERENCE REGULAR registration fee; extra tickets may not be available at the Registration Desk) Adult #______ ticket(s) @ $40 =$_________ child #______ ticket(s) @ $10 =$_________ EP Society Membership (qualifies registrant for reduced rates for this conference) ___ Regular, $40 ___ Student, $10 =$_________ FOR EP SOCIETY MEMBERS ONLY One year subscription to journal BioSystems (usually $575) ___ $75 =$_________ Send journal subscription to the following address: ___________________________________________________ ___________________________________________________ ___________________________________________________ ___________________________________________________ ___________________________________________________ ___________________________________________________ TOTAL (US dollars) $____________ METHOD OF PAYMENT ___ Check (payable to the Evolutionary Programming Society in US Funds only) ___ MasterCard ___ VISA #__________________________________________ Expiration Date ____________________ Signature of card holder ______________________________________________ Note: Students must submit with their registration a photocopy of their valid student ID or a letter from a professor. Mail EP96 Registration Natural Selection Inc. Attn: Bill Porto 3333 N. Torrey Pines Ct. Ste. 200 La Jolla CA 92037 Fax 619-455-1560 World Wide Web (WWW) For up-to-date conference information: http://www.owego.com/~pja/ep96.html -- +----------------------------------------------------------------------------+ | Peter J. Angeline, PhD | | | Advanced Technologies Dept. | | | Loral Federal Systems | | | State Route 17C | Why should I be limited to just | | Mail Drop 0210 | a CAUSAL chain of events? | | Owego, NY 13827-3994 | | | Voice: (607)751-4109 | - Anonymous | | Fax: (607)751-6025 | | | Email: pja@lfs.loral.com | | +----------------------------------------------------------------------------+ ------------------------------ Date: Sun, 14 Jan 96 14:43:21 EST From: Ibrahim Fahmi Imam Subject: CFP: AAAI-96 WS on Intelligent Adaptive Agents C A L L F O R P A P E R S AA AA AA IIII IIII AA AA AAAA AAAA AAAA II II AAAA AAAA AA AA AA AA AA AA II __ II AA AA AA AA AAAAAAAA AAAAAAAA AAAAAAAA II __ II AAAAAAAA AAAAAAAA AA AA AA AA AA AA II II AA AA AA AA AA AA AA AA AA AA IIII IIII AA AA AA AA AAAI-96 International Workshop on Intelligent Adaptive Agents (IAA-96) August 4-8, 1996, Portland, Oregon In recent years, researchers from different fields have pushed toward greater flexibility and intelligent adaptation in their systems. The development of intelligent adaptive agents has been rapidly evolving in many fields of science. Such systems should have the capability of dynamically adapting their parameters, improve their knowledge-base or method of operation in order to accomplish a set of tasks. This workshop will focus on intelligent adaptation and its relationship to other fields of interest. Research issues that are of interest to the workshop include but are not limited to: 1) Analyzing the role of adaptation in planning, execution monitoring, and problem-solving; 2) Adaptive control in real-world engineering systems; 3) Analyzing the computational cost of adaptation vs. system robustness; 4) Controlling the adaptive process (what is the strategy? what is needed?, what is expected?, etc.); 5) Adaptive mechanisms in an open agent society; 6) Adaptation in distributed systems; The workshop seeks high quality submission in these areas. Researchers interested in submitting papers should explain the adaptive process in light of one or more of the issues presented above. Papers with real-world applications are strongly encouraged. Please send any questions to: Ibrahim F. Imam at iimam@aic.gmu.edu Program Committee Members Gerald DeJong, University of Illinois at Urbana-Champaign, USA Tim Finin, University of Maryland Baltimore County, USA Brian Gaines, University of Calgary, Canada Diana Gordon, Naval Research Laboratory, USA Yves Kodratoff, Universite de Paris Sud, France Ryszard Michalski, George Mason University, USA Ashwin Ram, Georgia Institute of Technology, USA Nigel Shadbolt, University of Nottingham, England Reid Simmons, Carnegie Mellon University, USA Walter Van de Velde, Vrije Universiteit Brussel, Belgium Brad Whitehall, United Technologies Research Center, USA Stefan Wrobel, GMD, Germany Submission Information Paper submissions should not exceed eight single-spaced pages, with 1 inch margins, 12pt font. The first page must show the title, authors' names, full surface mail addresses, fax number (if possible), email addresses, short abstract (does not exceed 200 words), and a list of key words (up to 5). Electronic submissions are strongly encouraged and should be sent to the updated email address specified in the workshop World-Wide Web page. Otherwise, contact the workshop chair at (iimam@aic.gmu.edu) for mailing arrangements. An extended version of the CFP can be found in: http://www.mli.gmu.edu/~iimam/aaai96.html Important Dates Submission Deadline: March 18, 1996 Notification Date: April 15, 1996 Camera-Ready Due: May 13, 1996 Workshop: August 4, 1996 The workshop is a one day workshop. ------------------------------ Date: Sun, 14 Jan 1996 19:51:41 -0500 (EST) From: Russell Greiner Subject: CFP: Relevance in Knowledge Representation and Reasoning KR'96 Pre-Conference Workshop on Relevance in Knowledge Representation and Reasoning 3-4 November, 1996 Boston, Massachusetts C A L L F O R P A P E R S http://www.research.att.com/orgs/ssr/people/levy/rrr-cfp.html Essentially all reasoning systems use a corpus of information to reach appropriate conclusions. For example, deductive systems use initial theories (possibly encoded as predicate calculus statements) from which they draw conclusions, probabilistic systems use prior distributions (possibly encoded as a Bayesian network) to compute event probabilities, and abductive processes produce explanations based on both background theories and observations. With too little information, these systems clearly cannot work correctly. Surprisingly, too *much* information is also problematic, as it too can cause significant degradation in system performance. It is therefore critical to determine what information is irrelevant, to know what can be ignored or downplayed when considering a specific task (e.g., a specific query, or distribution of queries, to the system, or a specific observation to be explained). In some cases, ignoring irrelevant information is needed in order to draw the correct conclusions. There are many forms of irrelevance. In some contexts, the initial theory may include more information than the task requires, or information at a level of granularity that is more detailed than necessary. Here, the system may perform more effectively if it ignores or deletes certain irrelevant facts or if it ignores certain distinctions made in the representation. Another flavor of irrelevance arises during the course of reasoning: A reasoning process can ignore certain intermediate results, once it has established that they will not contribute to the eventual answer. This workshop follows the very eclectic 1994 Relevance Symposium, which investigated the notion of relevance across various fields of Artificial Intelligence and Computer Science. The current workshop, however, will focus on the use of relevance in knowledge representation and reasoning, specifically, on understanding different forms of irrelevance, and exploiting this "relevance information" to improve the performance of reasoning systems. Submissions are requested in areas relating to relevance in KR&R, including, but not limited to, the following: o Speeding up inference using relevance reasoning. o Relevance in probabilistic reasoning. o Relevance in explanation. o Relationships between relevance and belief revision and updates. o Relevance reasoning as a basis for abstraction and reformulation. o Using relevance of information to enable drawing appropriate conclusions. o Applications of relevance reasoning. o Reasoning about relevance of information, and foundations of relevance reasoning. Submission Information ====================== Authors wishing to present a paper should submit an extended abstract of at most 5000 words. Accepted participants will be invited to submit full papers for the workshop proceedings, which will be distributed to the workshop participants. Persons wishing to attend the workshop and not to present papers should submit a 1--2 page research summary that includes a list of relevant publications. Authors are encouraged to submit PostScript versions of their paper by email to either Russ Greiner (greiner@scr.siemens.com) or Alon Levy (levy@research.att.com). Authors unable to submit by email should send 4 copies of their paper to the address below. All submissions should be received by July 8, 1996. Please be sure to include e-mail address, telephone number and mailing address of the principal author. In case of multiple authors, please indicate which authors wish to participate. Notification of acceptance or rejection will be mailed to the principal author by August 16, 1996. Camera-ready copies of papers accepted for inclusion in the proceedings will be due September 17, 1996. Address for hardcopy submissions: Russell Greiner Siemens Corporate Research, Inc 755 College Road East Princeton, NJ 08540-6632 Important Dates =============== - Submissions due: July 8, 1996. - Notification of acceptance August 16, 1996. - Final version due September 17, 1996. - Workshop dates November 3-4, 1996. Program Chairs: =============== Russ Greiner (Siemens Corporate Research, greiner@scr.siemens.com) Alon Levy (AT&T Bell Laboratories, levy@research.att.com) Program Committee: ================== Adnan Darwiche (Rockwell) Jim Delgrande (Simon Frasier University) Daphne Koller (Stanford University) Gerhard Lakemeyer (University of Bonn) Alberto Mendelzon (University of Toronto) Devika Subramanian (Rice University) ------------------------------ Date: Sun, 21 Jan 1996 15:19:28 +0300 (MEST) From: Ethem Alpaydin Subject: CFP: TAINN'96, Conf on AI & NN (Istanbul/Turkey) Call for Papers TAINN'96, Istanbul 5th Turkish Symposium on Artificial Intelligence and Neural Networks To be held at Istanbul Technical University, Macka Campus June 27 - 28, 1996 Jointly-organized by Istanbul Technical University and Bogazici University. SPONSORS Istanbul Technical University, Bogazici University, and Turkish Scientific and Technical Research Council (Tubitak) IN COOPERATION WITH IEEE Computer Society Turkey Chapter, ACM SIGART Bilkent Chapter SCOPE Theory: Search, Knowledge Representation, Computational Learning Theory, Complexity Theory, Dynamical Systems, Combinatorial Optimization, Function Approximation, Estimation, Machine Learning, Machine Discovery, Social and Philosophical Issues. Algorithms and Architectures: Learning Algorithms, Multilayer Perceptrons, Recurrent Networks, Decision Trees, Genetic and Evolutionary Algorithms, Fuzzy Logic, Heuristic Search Methods, Symbolic Reasoning. Applications: Expert Systems, Natural Language Processing, Computer Vision, Image Processing, Speech Recognition Coding and Synthesis, Handwriting Recognition, Time-Series Prediction, Medical Processing, Financial Analysis, Music Processing, Control, Navigation, Path Planning, Automated Theorem Proving, Symbolic Algebraic Computation. Cognitive and Neuro Sciences: Human Learning, Memory and Language, Perception, Psychophysics, Computational Models. Implementation: Simulation Tools, Parallel Processing, Analog and Digital VLSI, Neurocomputing Systems. ORGANIZING COMMITTEE E. Alpaydin (Bogazici), U. Cilingiroglu (ITU), F. Gurgen (Bogazici), C. Guzelis (ITU) TECHNICAL COMMITTEE A.H. Abdel Wahab (Egypt), L. Akarun (Bogazici), L. Akin (Bogazici), V. Akman (Bilkent), F. Alpaslan (METU), K. Altinel (Bogazici), V. Atalay (METU), C. Bozsahin (METU), S. Canu (Compiegne, France), E. Celebi (ITU), I. Cicekli (Bilkent), K. Ciliz (Bogazici), D. Cohn (Harlequin, USA), D. Davenport (Bilkent), C. Dichev (BAS, Bulgaria), A. Erkmen (METU), G. Ernst (Case Western Reserve, USA), A. Fatholahzadeh (Supelec, France), Z. Ghahramani (Toronto, Canada), H. Ghaziri (Beirut, Lebanon), C. Goknar (ITU), M. Guler (METU), A. Guvenir (Bilkent), U. Halici (METU), M. Jabri (Sydney, Australia), M. Jordan (MIT, USA), S. Kocabas (Tubitak MAM-ITU), S. Kuru (Bogazici), K. Oflazer (Bilkent), R. Parikh (CUNY, USA), F. Masulli (Genova, Italy), M. de la Maza (MIT, USA), R. Murray-Smith (DaimlerBenz, Germany), Y. Ozturk (Ege), E. Oztemel (Tubitak MAM-SAU), F. Pekergin (EHEI, France), B. Sankur (Bogazici), A.F. Savaci (ITU), C. Say (Bogazici), L. Shastri (ICSI, USA), M. Sungur (METU), E. Tulunay (METU), G. Ucoluk (METU), N. Yalabik (METU), W. Zadrozny (IBM, USA). PAPER SUBMISSION Submit three hard-copies of full papers in English or Turkish limited to 10 pages in 12pt size or poster papers limited to 4 pages along with 5 keywords by March 1, 1996 to YZYSA'96/TAINN'96, Department of Computer Engineering, Bogazici University, Bebek TR-80815 Istanbul Turkey Accepted papers will be printed in the proceedings. The symposium will also host special sessions on certain subtopics. Proposals by qualified individuals interested in chairing one of these is solicited. The goal is to provide a forum for researchers to better focus on a certain subtopic and discuss important issues. Individuals proposing have the following responsibilities: o Arranging presentations by experts of the topic o Moderating or leading the session o Writing an overview of the topic and the session for the proceedings. Mail proposals by March 1, 1996 to YZYSA'96/TAINN'96, Faculty of Electrical and Electronics Engineering, Istanbul Technical University, Maslak TR-80626 Istanbul Turkey MORE INFORMATION Email: tainn96@boun.edu.tr URL: http://www.cmpe.boun.edu.tr/~tainn96 * Participation from Eastern European, Balkan, and Middle-East countries is especially solicited. ------------------------------ End of ML-LIST (Digest format) **************************************** From krista@torus.hut.fi Mon Jan 22 09:04:57 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 09:04:51 -0600; AA01208 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sat, 20 Jan 96 22:07:10 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab15597; 19 Jan 96 10:48:51 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab15529; 19 Jan 96 10:08:54 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa21715; 19 Jan 96 10:08:16 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa24779; 19 Jan 96 7:35:31 EST Received: from santra.hut.fi by RI.CMU.EDU id aa11406; 19 Jan 96 7:35:03 EST Received: from nucleus (nucleus.hut.fi [130.233.168.42]) by santra.hut.fi (8.7.3/8.7.3) with SMTP id OAA22524; Fri, 19 Jan 1996 14:33:51 +0200 (EET) Received: by nucleus (950215.SGI.8.6.10/940406.SGI) id OAA03195; Fri, 19 Jan 1996 14:33:43 +0200 Date: Fri, 19 Jan 1996 14:33:42 +0200 (EET) From: Krista Lagus X-Sender: krista@nucleus Reply-To: websom@nodulus.hut.fi To: Connectionists@cs.cmu.edu Cc: websom@nodulus.hut.fi Subject: A novel SOM-based approach to free-text mining Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII A novel SOM-based approach to free-text mining 19.1.1996 -- WEBSOM demo for newsgroup exploration Welcome to test the document exploration tool WEBSOM. An ordered map of the information space is provided: similar documents lie near each other on the map. The order helps in finding related documents once any interesting document is found. Currently a demo for browsing the 4900 articles that have appeared in the Usenet newsgroup comp.ai.neural-nets since 19.6.1995 is available in the WWW address http://websom.hut.fi/websom/ The WEBSOM home pages also contain an article describing the WEBSOM method and documentation of the demo. The demonstration requires a graphical WWW browser (such as Mosaic or Netscape), but the documentation can be read also with other browsers. The WEBSOM team: Timo Honkela Samuel Kaski Krista Lagus Teuvo Kohonen Helsinki University of Technology Neural Networks Research Centre Rakentajanaukio 2C FIN-02150 Espoo Finland email: websom@websom.hut.fi From trevor@mallet.Stanford.EDU Mon Jan 22 09:05:02 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 09:04:59 -0600; AA01222 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sat, 20 Jan 96 22:07:12 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa16887; 20 Jan 96 8:52:52 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa16867; 20 Jan 96 8:34:44 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa01916; 20 Jan 96 8:33:55 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id br02663; 19 Jan 96 21:38:59 EST Received: from mallet.Stanford.EDU by CS.CMU.EDU id aa11235; 19 Jan 96 20:39:45 EST Received: (from trevor@localhost) by mallet.Stanford.EDU (8.7.1/8.7.1) id RAA13247 for Connectionists@CS.CMU.EDU; Fri, 19 Jan 1996 17:15:56 -0800 (PST) Date: Fri, 19 Jan 1996 17:15:56 -0800 (PST) From: Trevor Hastie Message-Id: <199601200115.RAA13247@mallet.Stanford.EDU> To: Connectionists@cs.cmu.edu Subject: Regression and Classification course ************ SHORT COURSE ANNOUNCEMENT ********** MODERN REGRESSION AND CLASSIFICATION May 9-10, 1996 Stanford Park Hotel, Menlo Park ************************************************* A two-day course on widely applicable statistical methods for modelling and prediction, featuring Professor Trevor Hastie and Professor Robert Tibshirani Stanford University University of Toronto This two day course covers modern tools for statistical prediction and classification. We start from square one, with a review of linear techniques for regression and classification, and then take attendees through a tour of: o Flexible regression techniques o Classification and regression trees o Neural networks o Projection pursuit regression o Nearest Neighbor methods o Learning vector quantization o Wavelets o Bootstrap and cross-validation We will also illustrate software tools for implementing the methods. Our objective is to provide attendees with the background and knowledge necessary to apply these modern tools to solve their own real-world problems. The course is geared for: o Statisticians o Financial analysts o Industrial managers o Medical and Quantitative researchers o Scientists o others interested in prediction and classification Attendees should have an undergraduate degree in a quantitative field, or have knowledge and experience working in such a field. For more details on the course, how to register, price etc: o point your web browser to: http://playfair.stanford.edu/~trevor/mrc.html OR send a request by o FAX to Prof. T. Hastie at (415) 326-0854, OR o email to trevor@playfair.stanford.edu From davec@cogs.susx.ac.uk Mon Jan 22 09:05:46 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 09:05:42 -0600; AA01252 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sat, 20 Jan 96 22:07:08 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa15597; 19 Jan 96 10:47:27 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa15529; 19 Jan 96 10:08:50 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa21710; 19 Jan 96 10:07:36 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa24491; 19 Jan 96 6:52:19 EST Received: from [139.184.48.11] by EDRC.CMU.EDU id aa11140; 19 Jan 96 6:51:00 EST Received: by rsuna.crn.cogs.susx.ac.uk (Smail3.1.29.1 #3) id m0tdFBq-000AhzC; Fri, 19 Jan 96 11:41 GMT Message-Id: Subject: MSc in Evolutionary and Adaptive Systems To: connectionists@cs.cmu.edu, alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, genetic@dcs.shef.ac.uk Date: Fri, 19 Jan 1996 11:41:06 +0000 (GMT) From: Dave Cliff Cc: Dave Cliff Mime-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Length: 5613 Please distribute: The University of Sussex School of Cognitive and Computing Sciences Graduate Research Centre (COGS GRC) Master of Science (MSc) Degree in EVOLUTIONARY AND ADAPTIVE SYSTEMS Applications are invited for entry in October 1996 to the Master of Science (MSc) degree in Evolutionary and Adaptive Systems. The degree can be taken in one year full-time, or part-time over two years. Students initially follow taught courses, as preparation for an individual research project leading to a Masters Thesis. This email gives a brief summary of the degree. For further details, see: World-wide web: http://www.cogs.susx.ac.uk/lab/adapt/easy_msc.html Anonymous ftp: ftp to cogs.susx.ac.uk cd to pub/users/davec get (in binary mode) easy_msc.ps.Z (69K) Or contact the address at the end of this email to request hard-copy. The MSc is sponsored in part by: BNR Europe Ltd, Hewlett-Packard, Millennium Interactive. BACKGROUND The past decade has seen the formation of new research fields, crossing traditional boundaries between biology, computer science, and cognitive science. Known variously as Artificial Life, Simulation of Adaptive Behavior, and Evolutionary Computation, the common theme is a focus on adaptation in natural and artificial systems. This research has the potential both to further our understanding of living and adaptive mechanisms in nature, and to construct artificial systems which show the same flexibility, robustness, and capacity for adaptation as is seen in animals. The international research community is sufficiently large to support five series of biennial conferences on various aspects of the field (ICGA, ALife, ECAL, SAB, PPSN), and there are currently three international journals (all produced by MIT Press) for archival publication of significant research findings. The Evolutionary and Adaptive Systems (EASy) Research Group at the University of Sussex School of Cognitive and Computing Sciences (COGS) is now widely recognised as one of the world's foremost groups of researchers in this area, with approximately 35 people actively engaged in research. Students on the EASy MSc will be involved in this lively interdisciplinary environment. At the end of the course, students will have been trained to a standard where they are capable of pursuing doctoral research in any area of Evolutionary and Adaptive Systems; and of applying those techniques in industry. INTERNATIONAL STEERING GROUP M. A. Arbib (Uni. of Southern California, USA); M. Bedau (Reed College, USA); R. D. Beer (Case Western Reserve Uni, USA); R. A. Brooks (MIT, USA); H. Cruse (Universitat Bielefeld, Germany); K. De Jong (George Mason Uni., USA); D. Dennett (Tufts, USA); D. Floreano (LCT, Italy); J. Hallam (Uni. of Edinburgh, UK); I. Horswill (North Western Uni., USA); L. P. Kaelbling (Brown Uni., USA); C. G. Langton (Santa Fe Inst., USA); M. J. Mataric (Brandeis Uni., USA); J.-A. Meyer (Ecole Normale Superieure, France); G. F. Miller (MPIPF, Germany); R. Pfeiffer (Uni. of Zurich, Switz.); T. S. Ray (ATR, Japan); C. Reynolds (Silicon Graphics Inc, USA); H. L. Roitblat (Uni. of Hawaii, USA); T. Smithers (Euskal Herriko Unibertsitatae, Spain); L. Steels (VUB, Belgium); P. Todd (MPIPF, Germany); B. H. Webb (Uni. of Nottingham, UK); S. W. Wilson (Rowland Inst., USA). FULL-TIME SYLLABUS Autumn Term (Oct--Dec) ---------------------- Four compulsory courses: Artificial Life Introduction to Computer Science Formal Computational Skills Adaptive Behavior in Animals and Robots Spring Term (Jan-Mar) --------------------- Two compulsory courses: Adaptive Systems Neural Networks Two options chosen from the following list (further options may become available; some options may not be available in some years): Simulation of Adaptive Behavior History and Philosophy of Adaptive Systems Development in Human and Artificial Life Computer Vision Philosophy of Cognitive Science Computational Neuroscience Summer (Apr-Aug) ---------------- Research project, which should include a substantial practical (programming) element, leading to submission of a 12000-word masters thesis. It is intended that there will be industrial involvement in some projects. SUSSEX FACULTY INVOLVED IN THE MSc Prof. H. G. Barrow; Prof. M. A. Boden; Dr. H. Buxton; R. Chrisley; Prof. A. J. Clark; Dr. D. Cliff; Dr. T. S. Collett; Dr. P. Husbands; Dr. D. Osorio; Dr. J. C. Rutkowska; Dr. D. S. Young. APPLICATION PROCEDURE Application forms are available from: Postgraduate Admissions Office Sussex House University of Sussex Brighton BN1 9RH England, U.K. Tel: +44 (0)1273 678412 Email: PG.Admissions@admin.susx.ac.uk Early application is encouraged: there are a limited number of places on the MSc. If you have any further queries about this degree, please contact: Dr D Cliff School of Cognitive and Computing Sciences University of Sussex Brighton BN1 9QH England, U.K. Tel: +44 (0)1273 678754 Fax: +44 (0)1273 671320 E-mail: davec@cogs.susx.ac.uk From fu@cis.ufl.edu Mon Jan 22 09:11:34 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 09:11:31 -0600; AA02182 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 19 Jan 96 21:03:36 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa14113; 18 Jan 96 16:58:25 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa14111; 18 Jan 96 16:39:56 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa20985; 18 Jan 96 16:39:47 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa15321; 18 Jan 96 14:49:40 EST Received: from whale.cis.ufl.edu by EDRC.CMU.EDU id aa07951; 18 Jan 96 14:48:38 EST Received: by whale.cis.ufl.edu (8.6.12/4.11) id OAA18615; Thu, 18 Jan 1996 14:49:32 -0500 Date: Thu, 18 Jan 1996 14:49:32 -0500 From: fu@cis.ufl.edu Mmdf-Warning: Parse error in original version of preceding line at DST.BOLTZ.CS.CMU.EDU Message-Id: <199601181949.OAA18615@whale.cis.ufl.edu> To: connectionists@cs.cmu.edu Subject: Special issue on Knowledge-Based Neural Networks Special Issue: Knowledge-Based Neural Networks {Knowledge-Based Systems, 8(6), December 1995} Guest Editor: LiMin Fu, University of Florida (Gainesville, USA) Introduction to knowledge-based neural networks L Fu Dynamically adding symbolically meaningful nodes to knowledge-based neural networks D W Opitz and J W Shavlik Recurrent neural networks and prior knowledge for sequence processing: A constrained nondeterministic approach P Frasconi, M Gori, and G Soda Initialization of neural networks by means of decision trees I Ivanova and M Kubat Extension of the temporal synchrony approach to dynamic variable binding in a connectionist inference system N S Park, D Robertson, and K Stenning Hybrid modeling in pattern recognition and control Jim Bezdek Survey and critique of techniques for extracting rules from trained artificial neural networks R. Andrewsm J Diederich, and A B Tickle ======================================================== Orders: Elsevier Science BV, Order Fulfilment Department, P.O. Box 211, 1000 AE, Amsterdam, The Netherlands. Tel: +31 (20) 485-3642 Fax: +31 (20) 485-3598 From gs@next2.ss.uci.edu Mon Jan 22 09:11:54 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 09:11:47 -0600; AA02237 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 19 Jan 96 21:03:31 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa12437; 17 Jan 96 18:06:34 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa12435; 17 Jan 96 17:50:49 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19959; 17 Jan 96 17:50:21 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa15022; 17 Jan 96 2:33:20 EST Received: from orion.oac.uci.edu by RI.CMU.EDU id aa29890; 17 Jan 96 2:32:32 EST Received: from next2.ss.uci.edu (next2.ss.uci.edu [128.200.18.141]) by orion.oac.uci.edu (8.7.1/8.7.1) with SMTP id XAA14323; Tue, 16 Jan 1996 23:31:55 -0800 (PST) Received: by next2.ss.uci.edu (NX5.67d/NX3.0S) id AA07036; Tue, 16 Jan 96 23:22:22 -0800 Date: Tue, 16 Jan 96 23:22:22 -0800 From: George Sperling Message-Id: <9601170722.AA07036@next2.ss.uci.edu> To: group1@orion.oac.uci.edu Subject: Conference Announcement TWENTY-FIRST ANNUAL INTERDISCIPLINARY CONFERENCE Teton Village, Jackson Hole, Wyoming January 28 - February 2, 1996 Organizer: George Sperling, University of California, Irvine The TWENTY-FIRST ANNUAL INTERDISCIPLINARY CONFERENCE will meet in Teton Village, Jackson Hole, Wyoming, January 28 - February 2, 1996. The conference covers a wide range of subjects in what has come to be called cognitive science, ranging from visual and auditory physiology and psychophysics to human information processing, cognition, learning and memory, to computational approaches to these problems including neural networks and artificial intelligence. The aim is to provide overview talks that are comprehensible and interesting to a wide scientific audience --such as one might fantasy would occur at a National or Royal Academy of Science if such organizations were indeed devoted to scientific interchange. Attendance is limited by the size of the conference facility to about 50 persons. The Conference begins with a reception on Sunday evening, January 28, at 6:00p. Regular sessions meet from Monday through Friday at 4:00p to 8:00p; rest of the day is free. On Friday, 8:00p, there is a banquet for participants. A preliminary program is appended. The conference hotel, the Inn at Jackson Hole, is directly at the base of the ski slopes, a short walk from the tram and other ski lifts. The Conference has arranged special room rates for registered participants. To reserve lodging, telephone The Inn 1-800-842-7666 and inform the desk that you are with the Interdisciplinary Conference (AIC). Other hotels, restaurants, ski rental facilities, shops, and cross country ski trails, are all within walking distance. There are flights directly to Jackson Hole AP (taxi or bus to the hotel). Alternatively, Jackson is a five-hour drive from Salt Lake City. Additional information about the conference, previous programs, etc, are available at the WWW site below. To attend the conference, fill out the online registration form or request hardcopy from the organizer, and send the registration fee ($100) to the address below. To be sure of receiving future mailings, return a copy of the registration form with your current address. Annual Interdisciplinary Conference c/o Prof. George Sperling Cognitive Science Dept., SST-6 University of California Irvine, CA 92717 E-mail: sperling@uci.edu http://www.socsci.uci.edu/cogsci/HIPLab/AIC (for info about AIC-21) http://www.jacksonhole.com/ski (info about Jackson, WY) http://www.socsci.uci.edu/cogsci (for info about UCI Cognitive Sciences) --------------------------------------------------------------------------- P.S. UCI Update from the organizer: In spite of the fiscal difficulties faced by the State of California, UCI continues to move forward (two Nobel Prizes in 1995) and the Department of Cognitive Science is flourishing. In fall, 1995, the Department of Cognitive Science will be recruiting for three faculty positions with considerable flexibility in areas. There is an opening for a graduate student and a postdoc in my lab, and there are excellent opportunities for graduate students in the department --see the enclosed announcement and the WWW site above. =========================================================================== TWENTY-FIRST ANNUAL INTERDISCIPLINARY CONFERENCE Teton Village, Jackson Hole, Wyoming January 26 - February 2, 1996 Organizer: George Sperling, University of California, Irvine Preliminary Schedule (16Jan96) Sunday, January 28: 6:00 - 7:30 p.m. ** Reception ** Registration, Appetizers, Snacks, Refreshments. Monday, January 29, 4:00 - 8:00 p.m. Auditory Biology and Psychophysics; Visual Physiology Karen Glendenning, Psychology, Florida State U. Hearing: A Comparative Perspective. Bruce Masterton, Psychology, Florida State U. Role of the Central Auditory System in Hearing. Sam Williamson, Physics, New York University. The Decay of Sensory Memory. Randy Blake, Psychol, Vanderbilt U. Tachistoscopic Review of Mark Berkley's Research. Adina Roskies, Dept. Neurol, Washington U Med. Topographic Targeting of Retinal Axons in Development. Tuesday, January 29, 4:00 - 8:00 p.m. Motion Perception: Physiology, Psychophysics Larry O'Keefe, Center for Neural Science, NYU. Motion Processing in Primate Visual Cortex. Scott Richman, Cognitive Sci., UCI. A Specialized Receptor for Moving Flicker? Erik Blaser, Cog. Sci, UC Irvine. When is Motion Motion? Sophie Wuerger, Communic & Neurosci, Keele U. Colour in Moving and Stationary Orientation Discrimination. George Sperling, Cognitive Science, UC Irvine. Model of Gain-Control in Motion Processing. Wednesday Feb. 1, 4:00 - 8:00 Visual Learning, Learning; Information Processing Lorraine Allan, Psychology, McMaster U. New Slants on the McCollough Effect. Shepard Siegel, Psychology, McMaster U. What Contingent Color Aftereffects Tell Us About Drug Addiction. Hal Pashler, Psychology, U Cal., San Diego. Dual-Task Bottlenecks: Structural or Strategic? Geoffrey Loftus, Brain and Cog Sci, MIT. Information Acquisition and Phenomenology. Bill Prinzmetal, Dept of Psychology, UC Berkely. The Phenomenology of Attention. Zhong-Lin Lu, Cogn. Sci., U Cal. Irvine. Salience Model of Spatial Attention. Thursday 4:00 - 8:00 Memory Tim McNamara, Psychology, Vanderbilt. Viewpoint Dependence in Human Spatial Memory. Roger Ratcliff & Gail McKoon, Psychology, Northwestern U. Models of RT and Word Identification Richard Shiffrin, Psychology, Indiana U. A Model for Implicit and Explicit Momory. Barbara Dosher, Cogn. Sci., U Cal. Irvine. Forgetting in Implicit and Explicit Memory Tasks. David Caulton, Natl Inst Health. Memory Retrieval Dynamics: Behavioral and Electrophysiological Approaches. Friday 4:00 - 8:00 p.m. Computational Issues Sandy Pentland, Media Lab., MIT. The Perception of Driving Intentions. Leonid Kontsevich, Smith-Kettlewell Eye Research Institute. The Role of Partial Similarity in 3D Vision. Misha Pavel, EE., Oregon Graduate Institute. The Role of Features in the Perception of Symmetry. Maria Kozhevnikov, Physics, Technion, Israel. A Mathematical Model of Conceptual Development. Shulamith Eckstein, Physics, Technion, Israel. A Dynamic Model of Cognitive Growth in a Population. * * * 8:00 Fireside Banquet at The Inn * * * From dnoelle@cs.ucsd.edu Mon Jan 22 09:12:01 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 09:11:58 -0600; AA02266 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 19 Jan 96 21:03:39 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa14212; 18 Jan 96 17:08:33 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa14115; 18 Jan 96 16:40:47 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa20991; 18 Jan 96 16:40:09 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa16379; 18 Jan 96 15:47:35 EST Received: from odin.ucsd.edu by EDRC.CMU.EDU id aa08216; 18 Jan 96 15:46:23 EST Received: from beowulf.ucsd.edu by odin.ucsd.edu; id AA15153 sendmail 5.67/UCSDPSEUDO.4-CS via SMTP Thu, 18 Jan 96 12:46:17 -0800 for connectionists@cs.cmu.edu Received: by beowulf (5.67/UCSDPSEUDO.4) id AA25271 for ; Thu, 18 Jan 96 12:40:16 -0800 Date: Thu, 18 Jan 96 12:40:16 -0800 From: David Noelle Message-Id: <9601182040.AA25271@beowulf> Subject: Cog Sci 96: Final Call For Papers Apparently-To: connectionists@cs.cmu.edu Eighteenth Annual Conference of the COGNITIVE SCIENCE SOCIETY July 12-15, 1996 University of California, San Diego La Jolla, California SECOND (AND FINAL) CALL FOR PAPERS DUE DATE: Thursday, February 1, 1996 CONTACT: cogsci96@cs.ucsd.edu EXECUTIVE SUMMARY OF CHANGES FROM ORIGINAL CFP After discussion with the advisory board, we decided to go with a three-tiered approach after all. There will be six page papers in the proceedings for both talks and posters. However, even if your paper/poster is not accepted, you will have a chance to submit a one page abstract for publication and poster presentation. Or, you may submit a one-page abstract initially (actually two pages in the submission format) for guaranteed acceptance. This is meant to accommodate the very different cultures of the component disciplines of the Society, while making a minimal change from previous years' formats. Also, this CFP provides a partial list of the program committee, the plenary speakers, a rough schedule for the paper reviewing process, and some keywords to aid in the process of reviewing your paper. INTRODUCTION The Annual Cognitive Science Conference began with the La Jolla Conference on Cognitive Science in August of 1979. The organizing committee of the Eighteenth Annual Conference would like to welcome members home to La Jolla. We plan to recapture the pioneering spirit of the original conference, extending our welcome to fields on the expanding frontier of Cognitive Science, including Artificial Life, Cognitive and Computational Neuroscience, Evolutionary Psychology, as well as the core areas of Anthropology, Computer Science, Linguistics, Neuroscience, Philosophy, and Psychology. As a change this year, we follow the example of Psychonomics and the Neuroscience Conferences and invite Members of the Society to submit short abstracts for guaranteed poster presentation at the conference. The conference will feature plenary addresses by invited speakers, invited symposia by leaders in their fields, technical paper sessions, a poster session, a banquet, and a Blues Party. San Diego is the home of the world-famous San Diego Zoo and Wild Animal Park, Sea World, the historic all-wooden Hotel Del Coronado, beautiful beaches, mountain areas and deserts, is a short drive from Mexico, and features a high Cappuccino Index. Bring the whole family and stay a while! PLENARY SESSIONS 1. "Controversies in Cognitive Science: The Case of Language" Stephen Crain (UMD College Park) & Mark Seidenberg (USC) Moderated by Paul Smolensky (Johns Hopkins University) 2. "Tenth Anniversary of the PDP Books" Geoff Hinton (Toronto) Jay McClelland (CMU) Dave Rumelhart (Stanford) 3. "Frontal Lobe Development and Dysfunction in Children: Dissociations between Intention and Action" Adele Diamond (MIT) 4. "Reconstructing Consciousness" Paul Churchland (UCSD) PROGRAM COMMITTEE (a partial list): Garrison W. Cottrell (UCSD) -- Program Chair Farrell Ackerman (UCSD) -- Linguistics Tom Albright (Salk Institute) -- Neuroscience Patricia Churchland (UCSD) -- Philosophy Roy D'Andrade (UCSD) -- Anthropology Charles Elkan (UCSD) -- Computer Science Catherine Harris (Boston U.) -- Psychology Doug Medin (Northwestern) -- Psychology Risto Miikkulainen (U. of Texas, Austin) -- Computer Science Kim Plunkett (Oxford) -- Psychology Martin Sereno (UCSD) -- Neuroscience Tim van Gelder (Indiana U. & U. of Melbourne) -- Philosophy GUIDELINES FOR PAPER SUBMISSIONS Novel research papers are invited on any topic related to cognition. Members of the Society may submit a one page abstract (two pages in double-spaced submission format) for poster presentation, which will be automatically accepted for publication in the proceedings. Submitted full-length papers will be evaluated through peer review with respect to several criteria, including originality, quality, and significance of research, relevance to a broad audience of cognitive science researchers, and clarity of presentation. Papers will be accepted for either oral or poster presentation, and will receive 6 pages in the proceedings in the final, camera-ready format. Papers that are rejected at this stage may be re-submitted (if the author is a Society member) as a one page abstract in the camera-ready format, due at the same date as camera-ready papers. Poster abstracts from non-members will be accepted, but the presenter should join the Society prior to presenting the poster. Papers accepted for oral presentation will be presented at the conference as scheduled talks. Papers accepted for poster presentation and one page abstracts will be presented at a poster session at the conference. All papers may present results from completed research as well as report on current research with an emphasis on novel approaches, methods, ideas, and perspectives. Posters may report on recent work to be published elsewhere that has not been previously presented at the conference. Authors should submit five (5) copies of the paper in hard copy form by Thursday, February 1, 1996, to: Dr. Garrison W. Cottrell Computer Science and Engineering 0114 FED EX ONLY: 3250 Applied Physics and Math University of California San Diego La Jolla, Ca. 92093-0114 phone for FED EX: 619-534-5948 (my secretary, Marie Kreider) If confirmation of receipt is desired, please use certified mail or enclose a self-addressed stamped envelope or postcard. DAVID MARR MEMORIAL PRIZES FOR EXCELLENT STUDENT PAPERS Papers with a student first author are eligible to compete for a David Marr Memorial Prize for excellence in research and presentation. The David Marr Prizes are accompanied by a $300.00 honorarium, and are funded by an anonymous donor. LENGTH Papers must be a maximum of eleven (11) pages long (excluding only the cover page but including figures and references), with 1 inch margins on all sides (i.e., the text should be 6.5 inches by 9 inches, including footnotes but excluding page numbers), double-spaced, and in 12-point type. Each page should be numbered (excluding the cover page). Template and style files conforming to these specifications for several text formatting programs, including LaTeX, Framemaker, Word, and Word Perfect are available by anonymous FTP from "cs.ucsd.edu" in the "pub/cogsci96/formats" directory. There is a self-explanatory subdirectory hierarchy under that directory for papers and posters. Formatting information is also available via the World Wide Web at the conference web page located at "http://www.cse.ucsd.edu/events/cogsci96/". Submitted abstracts should be two pages in submitted format, with the same margins as full papers. Style files for these are available at the same location as above. Final versions of papers and poster abstracts will be required only after authors are notified of acceptance; accepted papers may be published in a CD-ROM version of the proceedings. Abstracts will be available before the meeting from a WWW server. Final versions must follow the HTML style guidelines which will be made available to the authors of accepted papers and abstracts. This year we will again attempt to publish the proceedings in two modalities, paper and a CD-ROM version. Depending on a decision of the Governing Board, we may be switching completely from paper to CD-ROM publication in order to control escalating costs and permit use of search software. [Comments on this change should be directed to "alan@lrdc4.lrdc.pitt.edu" (Alan Lesgold, Secretary/Treasurer).] COVER PAGE Each copy of the submitted paper must include a cover page, separate from the body of the paper, which includes: 1. Title of paper. 2. Full names, postal addresses, phone numbers, and e-mail addresses of all authors. 3. An abstract of no more than 200 words. 4. Three to five keywords in decreasing order of relevance. The keywords will be used in the index for the proceedings. You may use the keywords from the attached list, or you may make up your own. Please try to give a primary discipline (or pair of disciplines) to which the paper is addressed (e.g., Psychology, Philosophy, etc.) 5. Preference for presentation format: Talk or poster, talk only, poster only. Poster only submissions should follow paper format, but be no more than 2 pages in this format (final poster abstracts will follow the same 2 column format as papers). Accepted papers will be presented as talks. Submitted posters by Society Members will be accepted for poster presentation, but may, at the discretion of the Program Committee, be invited for oral presentation. Non-members may join the Society at the time of submission. 6. A note stating if the paper is eligible to compete for a Marr Prize. DEADLINE Papers must be received by Thursday, February 1, 1996. Papers received after this date will be recycled. REVIEW SCHEDULE February 1: Papers due March 21: Decisions/Reviews Returned To Authors April 14: Final Papers & Abstracts Due CALL FOR SYMPOSIA (The call for symposia has been deleted here, as the deadline has passed.) CONFERENCE CHAIRS Edwin Hutchins and Walter Savitch PROGRAM CHAIR Garrison W. Cottrell Please direct email to "cogsci96@cs.ucsd.edu". KEYWORDS Please identify an appropriate major discipline for your work (try to name no more than two!) and up to three subareas from the following list. Anthropology Behavioral Ecology Cognition & Education Cognitive Anthropology Distributed Cognition Situated Cognition Social & Group Cognition Computer Science Artificial Intelligence Artificial Life Case-Based Learning Case-Based Reasoning Category & Concept Learning Category & Concept Representation Computer Aided Instruction Computer Human Interaction Computer Vision Connectionism Discovery-Based Learning Distributed Systems Explanation Generation Hybrid Representations Inference & Decision Making Intelligent Agents Machine Learning Memory Model-Based Reasoning Natural Language Generation Natural Language Learning Natural Language Processing Planning & Action Problem Solving Reasoning Heuristics Reasoning Under Time Constraints Robotics Rule-Based Reasoning Situated Cognition Speech Generation Speech Processing Text Comprehension & Translation Linguistics Cognitive Linguistics Discourse & Text Comprehension Generative Linguistics Language Acquisition & Development Language Generation Language Understanding Lexical Semantics Phonology & Word Recognition Pragmatics & Communication Psycholinguistics Sentence Processing Syntax Neuroscience Attention Brain Imaging Cognitive Neuroscience Computational Neuroscience Consciousness Memory Motor Control Language Acquisition & Development Language Generation Language Understanding Neuropsychology Neural Plasticity Perception & Recognition Planning & Action Spatial Processing Philosophy Philosophy Of Anthropology Philosophy Of Biology Philosophy Of Language Philosophy Of Mind Philosophy Of Neuroscience Philosophy Of Psychology Philosophy Of Science Psychology Analogical Reasoning Associative Learning Attention Behavioral Ecology Case-Based Learning Case-Based Reasoning Category & Concept Learning Category & Concept Representation Cognition & Education Consciousness Discourse & Text Comprehension Discovery-Based Learning Distributed Cognition Evolutionary Psychology Explanation Generation Imagery Inference & Decision Making Language Acquisition & Development Language Generation Language Understanding Lexical Semantics Memory Model-Based Reasoning Neuropsychology Perception & Recognition Phonology & Word Recognition Planning & Action Pragmatics & Communication Problem Solving Psycholinguistics Reasoning Heuristics Reasoning Under Time Constraints Rule-Based Reasoning Sentence Processing Situated Cognition Spatial Processing Syntactic Processing From alpaydin@boun.edu.tr Mon Jan 22 10:12:10 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 10:11:36 -0600; AB05948 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Mon, 22 Jan 96 10:11:28 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa19608; 21 Jan 96 23:40:59 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa19606; 21 Jan 96 23:27:10 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa03111; 21 Jan 96 23:26:44 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa19976; 21 Jan 96 8:32:17 EST Received: from hamlin.cc.boun.edu.tr by CS.CMU.EDU id aa18943; 21 Jan 96 8:31:47 EST Received: by hamlin.cc.boun.edu.tr (AIX 4.1/UCB 5.64/4.03) id AA11248; Sun, 21 Jan 1996 15:19:29 +0300 Date: Sun, 21 Jan 1996 15:19:28 +0300 (MEST) From: Ethem Alpaydin X-Sender: alpaydin@hamlin.cc.boun.edu.tr To: connectionists@cs.cmu.edu, ml@ics.uci.edu, kdd@gte.com, inductive@hermes.csd.unb.ca, DAI-List@ece.sc.edu, GA-List@aic.nrl.navy.mil, ai-stats@watstat.uwaterloo.ca, dbworld@lucy.cs.wisc.edu, intcon@phoenix.ee.unsw.edu.au, EP-LIST@magenta.me.fau.edu, alife@cognet.ucla.edu, hybrid-list@cs.ua.edu, neuron@CATTELL20.psych.upenn.edu, cogpsy@neuro.psy.soton.ac.uk, genetic-programming@cs.stanford.edu, gann@cs.iastate.edu, evolutionary-computing@mailbase.ac.uk, TIERRA@life.slhs.udel.edu, cells@tce.ing.uniroma1.it, colt@cs.uiuc.edu Subject: CFP: TAINN'96, Conf on AI & NN (Istanbul/Turkey) Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII Pre-S. We're sorry if you receive multiple copies of this message. ase forward * Please post * Please forward * Please post * Please forwa Call for Papers TAINN'96, Istanbul 5th Turkish Symposium on Artificial Intelligence and Neural Networks To be held at Istanbul Technical University, Macka Campus June 27 - 28, 1996 Jointly-organized by Istanbul Technical University and Bogazici University. SPONSORS Istanbul Technical University, Bogazici University, and Turkish Scientific and Technical Research Council (Tubitak) IN COOPERATION WITH IEEE Computer Society Turkey Chapter, ACM SIGART Bilkent Chapter SCOPE Theory: Search, Knowledge Representation, Computational Learning Theory, Complexity Theory, Dynamical Systems, Combinatorial Optimization, Function Approximation, Estimation, Machine Learning, Machine Discovery, Social and Philosophical Issues. Algorithms and Architectures: Learning Algorithms, Multilayer Perceptrons, Recurrent Networks, Decision Trees, Genetic and Evolutionary Algorithms, Fuzzy Logic, Heuristic Search Methods, Symbolic Reasoning. Applications: Expert Systems, Natural Language Processing, Computer Vision, Image Processing, Speech Recognition Coding and Synthesis, Handwriting Recognition, Time-Series Prediction, Medical Processing, Financial Analysis, Music Processing, Control, Navigation, Path Planning, Automated Theorem Proving, Symbolic Algebraic Computation. Cognitive and Neuro Sciences: Human Learning, Memory and Language, Perception, Psychophysics, Computational Models. Implementation: Simulation Tools, Parallel Processing, Analog and Digital VLSI, Neurocomputing Systems. ORGANIZING COMMITTEE E. Alpaydin (Bogazici), U. Cilingiroglu (ITU), F. Gurgen (Bogazici), C. Guzelis (ITU) TECHNICAL COMMITTEE A.H. Abdel Wahab (Egypt), L. Akarun (Bogazici), L. Akin (Bogazici), V. Akman (Bilkent), F. Alpaslan (METU), K. Altinel (Bogazici), V. Atalay (METU), C. Bozsahin (METU), S. Canu (Compiegne, France), E. Celebi (ITU), I. Cicekli (Bilkent), K. Ciliz (Bogazici), D. Cohn (Harlequin, USA), D. Davenport (Bilkent), C. Dichev (BAS, Bulgaria), A. Erkmen (METU), G. Ernst (Case Western Reserve, USA), A. Fatholahzadeh (Supelec, France), Z. Ghahramani (Toronto, Canada), H. Ghaziri (Beirut, Lebanon), C. Goknar (ITU), M. Guler (METU), A. Guvenir (Bilkent), U. Halici (METU), M. Jabri (Sydney, Australia), M. Jordan (MIT, USA), S. Kocabas (Tubitak MAM-ITU), S. Kuru (Bogazici), K. Oflazer (Bilkent), R. Parikh (CUNY, USA), F. Masulli (Genova, Italy), M. de la Maza (MIT, USA), R. Murray-Smith (DaimlerBenz, Germany), Y. Ozturk (Ege), E. Oztemel (Tubitak MAM-SAU), F. Pekergin (EHEI, France), B. Sankur (Bogazici), A.F. Savaci (ITU), C. Say (Bogazici), L. Shastri (ICSI, USA), M. Sungur (METU), E. Tulunay (METU), G. Ucoluk (METU), N. Yalabik (METU), W. Zadrozny (IBM, USA). PAPER SUBMISSION Submit three hard-copies of full papers in English or Turkish limited to 10 pages in 12pt size or poster papers limited to 4 pages along with 5 keywords by March 1, 1996 to YZYSA'96/TAINN'96, Department of Computer Engineering, Bogazici University, Bebek TR-80815 Istanbul Turkey Accepted papers will be printed in the proceedings. The symposium will also host special sessions on certain subtopics. Proposals by qualified individuals interested in chairing one of these is solicited. The goal is to provide a forum for researchers to better focus on a certain subtopic and discuss important issues. Individuals proposing have the following responsibilities: o Arranging presentations by experts of the topic o Moderating or leading the session o Writing an overview of the topic and the session for the proceedings. Mail proposals by March 1, 1996 to YZYSA'96/TAINN'96, Faculty of Electrical and Electronics Engineering, Istanbul Technical University, Maslak TR-80626 Istanbul Turkey MORE INFORMATION Email: tainn96@boun.edu.tr URL: http://www.cmpe.boun.edu.tr/~tainn96 * Participation from Eastern European, Balkan, and Middle-East countries is especially solicited. From harnad@cogsci.soton.ac.uk Mon Jan 22 10:12:13 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 10:11:49 -0600; AA05957 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Mon, 22 Jan 96 10:11:38 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab19627; 21 Jan 96 23:51:24 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa19613; 21 Jan 96 23:29:42 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa03126; 21 Jan 96 23:29:28 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa23905; 21 Jan 96 17:06:59 EST Received: from beech.soton.ac.uk by CS.CMU.EDU id aa21151; 21 Jan 96 17:06:45 EST Received: from cogsci.soton.ac.uk (neuro.psy.soton.ac.uk [152.78.226.32]) by beech.soton.ac.uk (8.6.12/hub-8.5a) with SMTP id WAA09798; Sun, 21 Jan 1996 22:05:24 GMT Received: from cogsci.ecs.soton.ac.uk by cogsci.soton.ac.uk (5.x/SMI-SVR4) id AA11942; Sun, 21 Jan 1996 21:59:02 GMT From: Stevan Harnad Date: Sun, 21 Jan 96 22:06:14 GMT Message-Id: <3919.9601212206@cogsci.ecs.soton.ac.uk> To: biomch-l@hearn.bitnet, cogneuro@ptolemy-ethernet.arc.nasa.gov, neuromotor-control@ai.mit.edu Subject: Directed Movement: BBS Call for Commentators Cc: cogni-info@univ-lyon1.fr, "Cogniscience\ Francophone" Below is the abstract of a forthcoming target article on: SPEED/ACCURACY TRADEOFFS IN TARGET DIRECTED MOVEMENTS By Rejean Plamondon & Adel M. Alimi This article has been accepted for publication in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal providing Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. Commentators must be current BBS Associates or nominated by a current BBS Associate. To be considered as a commentator for this article, to suggest other appropriate commentators, or for information about how to become a BBS Associate, please send email to: bbs@soton.ac.uk or write to: Behavioral and Brain Sciences Department of Psychology University of Southampton Highfield, Southampton SO17 1BJ UNITED KINGDOM http://www.princeton.edu/~harnad/bbs.html gopher://gopher.princeton.edu:70/11/.libraries/.pujournals ftp://ftp.princeton.edu/pub/harnad/BBS To help us put together a balanced list of commentators, please give some indication of the aspects of the topic on which you would bring your areas of expertise to bear if you were selected as a commentator. An electronic draft of the full text is available for inspection by anonymous ftp (or gopher or world-wide-web) according to the instructions that follow after the abstract. ____________________________________________________________________ SPEED/ACCURACY TRADEOFFS IN TARGET DIRECTED MOVEMENTS Rejean Plamondon & Adel M. Alimi Ecole Polytechnique de Montreal Laboratoire Scribens Departement de genie Electrique et de genie informatique C.P. 6079, Succ. "Centre-Ville" Montreal PQ H3C 3A7 ha03@music.mus.polymtl.ca KEYWORDS: Speed/accuracy tradeoffs, Fitts' law, central limit theorem, velocity profile, delta-lognormal law, quadratic law, power law. ABSTRACT: This paper presents a critical survey of the scientific literature dealing with the speed/accuracy tradeoffs of rapid-aimed movements. It highlights the numerous mathematical and theoretical interpretations that have been proposed over recent decades from the different studies that have been conducted on this topic. Although the variety of points of view reflects the richness of the field as well as the high degree of interest that such basic phenomena represent in the understanding of human movements, it questions the validity of many models with respect to their capacity to explain all the basic observations consistently reported in the field. In this perspective, this paper summarizes the kinematic theory of rapid human movements, proposed recently by the first author, and analyzes its predictions in the context of speed/accuracy tradeoffs. Numerous data available from the scientific literature are reanalyzed and reinterpreted in the context of this new theory. It is shown that the various aspects of the speed/accuracy tradeoffs can be taken into account by considering the asymptotic behavior of a large number of coupled linear systems, from which a delta-lognormal law can be derived, to describe the velocity profile of an end-effector driven by a neuromuscular synergy. This law not only describes velocity profiles almost perfectly, but it also predicts the kinematic properties of simple rapid movements and provides a consistent framework for the analysis of different types of rapid movements using a quadratic (or power) law that emerges from the model. -------------------------------------------------------------- To help you decide whether you would be an appropriate commentator for this article, an electronic draft is retrievable by anonymous ftp from ftp.princeton.edu according to the instructions below (the filename is bbs.glenberg). Please do not prepare a commentary on this draft. Just let us know, after having inspected it, what relevant expertise you feel you would bring to bear on what aspect of the article. ------------------------------------------------------------- These files are also on the World Wide Web and the easiest way to retrieve them is with Netscape, Mosaic, gopher, archie, veronica, etc. Here are some of the URLs you can use to get to the BBS Archive: http://www.princeton.edu/~harnad/bbs.html http://cogsci.soton.ac.uk/~harnad/bbs.html gopher://gopher.princeton.edu:70/11/.libraries/.pujournals ftp://ftp.princeton.edu/pub/harnad/BBS/bbs.glenberg ftp://cogsci.soton.ac.uk/pub/harnad/BBS/bbs.glenberg To retrieve a file by ftp from an Internet site, type either: ftp ftp.princeton.edu or ftp 128.112.128.1 When you are asked for your login, type: anonymous Enter password as queried (your password is your actual userid: yourlogin@yourhost.whatever.whatever - be sure to include the "@") cd /pub/harnad/BBS To show the available files, type: ls Next, retrieve the file you want with (for example): get bbs.glenberg When you have the file(s) you want, type: quit ---------- Where the above procedure is not available there are two fileservers: ftpmail@decwrl.dec.com and bitftp@pucc.bitnet that will do the transfer for you. To one or the other of them, send the following one line message: help for instructions (which will be similar to the above, but will be in the form of a series of lines in an email message that ftpmail or bitftp will then execute for you). ------------------------------------------------------------- From harnad@cogsci.soton.ac.uk Mon Jan 22 10:12:15 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 22 Jan 96 10:11:42 -0600; AA05955 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Mon, 22 Jan 96 10:11:33 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa19627; 21 Jan 96 23:49:18 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa19610; 21 Jan 96 23:28:01 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa03117; 21 Jan 96 23:26:58 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa23827; 21 Jan 96 17:03:12 EST Received: from beech.soton.ac.uk by RI.CMU.EDU id aa20458; 21 Jan 96 17:02:32 EST Received: from cogsci.soton.ac.uk (neuro.psy.soton.ac.uk [152.78.226.32]) by beech.soton.ac.uk (8.6.12/hub-8.5a) with SMTP id WAA09693; Sun, 21 Jan 1996 22:02:07 GMT Received: from cogsci.ecs.soton.ac.uk by cogsci.soton.ac.uk (5.x/SMI-SVR4) id AA11779; Sun, 21 Jan 1996 21:55:47 GMT From: Stevan Harnad Date: Sun, 21 Jan 96 22:03:00 GMT Message-Id: <3879.9601212203@cogsci.ecs.soton.ac.uk> To: PHILOS-L@liverpool.ac.uk, connectionists@cs.cmu.edu, Soc Phil Psych Subject: Learning/Representation: BBS Call for Commentators Below is the abstract of a forthcoming target article on: COMPUTATION, REPRESENTATION AND LEARNING by Andy Clark and Chris Thronton This article has been accepted for publication in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal providing Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. Commentators must be current BBS Associates or nominated by a current BBS Associate. To be considered as a commentator for this article, to suggest other appropriate commentators, or for information about how to become a BBS Associate, please send email to: bbs@soton.ac.uk or write to: Behavioral and Brain Sciences Department of Psychology University of Southampton Highfield, Southampton SO17 1BJ UNITED KINGDOM http://www.princeton.edu/~harnad/bbs.html gopher://gopher.princeton.edu:70/11/.libraries/.pujournals ftp://ftp.princeton.edu/pub/harnad/BBS To help us put together a balanced list of commentators, please give some indication of the aspects of the topic on which you would bring your areas of expertise to bear if you were selected as a commentator. An electronic draft of the full text is available for inspection by anonymous ftp (or gopher or world-wide-web) according to the instructions that follow after the abstract. ____________________________________________________________________ TRADING SPACES: COMPUTATION, REPRESENTATION AND THE LIMITS OF UNINFORMED LEARNING Andy Clark Philosophy/Neuroscience/Psychology Program, Washington University in St Louis, Campus Box 1073, St Louis, MO-63130, USA andy@twinearth.wustl.edu Chris Thornton, Cognitive and Computing Sciences, University of Sussex, Brighton, BN1 9QH, UK Chris.Thornton@cogs.sussex.ac.uk KEYWORDS: Learning, connectionism, statistics, representation, search ABSTRACT: Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic re-coding of the data. The space of possible re-codings is, however, infinitely large - it is the space of applicable Turing machines. As a result, mappings which pivot on such attenuated regularities cannot, in general, be found by brute force search. The class of problems which present such mappings we call the class of `type-2 problems'. Type-1 problems, by contrast, present tractable problems of search insofar as the relevant regularities can be found by sampling the input data as originally coded. Type-2 problems, we suggest, present neither rare nor pathological cases. They are rife in biologically realistic settings and in domains ranging from simple animat behaviors to language acquisition. Not only are such problems rife - they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of re-coding biases so as to reduce specific type-2 problems to (tractable) type-1 mappings. Such a heavy duty nativism is no doubt sometimes plausible. But we believe there are other, more general mechanisms also at work. Such mechanisms provide general (not task-specific) strategies for managing problems of type-2 complexity. Several such mechanisms are investigated. At the heart of each is a fundamental ploy viz. the maximal exploitation of states of representation already achieved by prior (type-1) learning so as to reduce the amount of subsequent computational search. Such exploitation both characterises and helps make unitary sense of a diverse range of mechanisms. These include simple incremental learning (Elman 1993), modular connectionism (Jacobs, Jordan and Barto 1991), and the developmental hypothesis of `representational redescription' (Karmiloff-Smith A Functional 1979, Karmiloff-Smith PDP 1992). In addition, the most distinctive features of human cognition---language and culture---may themselves be viewed as adaptations enabling this representation/computation trade-off to be pursued on an even grander scale. -------------------------------------------------------------- To help you decide whether you would be an appropriate commentator for this article, an electronic draft is retrievable by anonymous ftp from ftp.princeton.edu according to the instructions below (the filename is bbs.clark). Please do not prepare a commentary on this draft. Just let us know, after having inspected it, what relevant expertise you feel you would bring to bear on what aspect of the article. ------------------------------------------------------------- These files are also on the World Wide Web and the easiest way to retrieve them is with Netscape, Mosaic, gopher, archie, veronica, etc. Here are some of the URLs you can use to get to the BBS Archive: http://www.princeton.edu/~harnad/bbs.html http://cogsci.soton.ac.uk/~harnad/bbs.html gopher://gopher.princeton.edu:70/11/.libraries/.pujournals ftp://ftp.princeton.edu/pub/harnad/BBS/bbs.clark ftp://cogsci.soton.ac.uk/pub/harnad/BBS/bbs.clark To retrieve a file by ftp from an Internet site, type either: ftp ftp.princeton.edu or ftp 128.112.128.1 When you are asked for your login, type: anonymous Enter password as queried (your password is your actual userid: yourlogin@yourhost.whatever.whatever - be sure to include the "@") cd /pub/harnad/BBS To show the available files, type: ls Next, retrieve the file you want with (for example): get bbs.clark When you have the file(s) you want, type: quit ---------- Where the above procedure is not available there are two fileservers: ftpmail@decwrl.dec.com and bitftp@pucc.bitnet that will do the transfer for you. To one or the other of them, send the following one line message: help for instructions (which will be similar to the above, but will be in the form of a series of lines in an email message that ftpmail or bitftp will then execute for you). ------------------------------------------------------------- From cns-cas@cns.bu.edu Wed Jan 24 02:12:41 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 24 Jan 96 02:12:21 -0600; AA23944 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 24 Jan 96 02:12:19 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa20744; 22 Jan 96 15:47:45 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa20742; 22 Jan 96 15:27:28 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa03869; 22 Jan 96 15:26:19 EST Received: from MAILBOX.SRV.CS.CMU.EDU by B.GP.CS.CMU.EDU id aa05043; 22 Jan 96 10:52:39 EST Received: from [128.197.61.100] by MAILBOX.SRV.CS.CMU.EDU id aa23973; 22 Jan 96 10:50:56 EST Received: from mac-duffee.bu.edu by cns.bu.edu (8.6.10/BU_Server-1.1) id KAA05031; Mon, 22 Jan 1996 10:43:56 -0500 Message-Id: <199601221543.KAA05031@cns.bu.edu> Date: Mon, 22 Jan 1996 10:47:23 -0500 From: CNS/CAS To: neuron@cattell.psych.upenn.edu, nl-kr@cs.rpi.edu, psyc@pucc.princeton.edu, ai-ed@sun.com, ai-medicine@medmail.Stanford.EDU, biophys@net.bio.net, compumed@sjuvm.stjohns.edu, cybsys-l@bingvmb.cc.binghamton.edu, cvnet@skivs.ski.org, dasp-l@earn.cvut.cz, connectionists@MAILBOX.SRV.CS.CMU.EDU, wisenet@UICVM.CC.UIC.EDU, medphys@radonc.duke.edu, cbt-general@virginia.edu, population-biology@net.bio.net Cc: cas-cns@cns.bu.edu Subject: B.U. Neural Systems Seminars Organization: Boston University - Dept. of Cognitive & Neural Systems Reply-To: cas-cns@cns.bu.edu X-Newsreader: Yet Another NewsWatcher 2.0.6b4 CENTER FOR ADAPTIVE SYSTEMS AND DEPARTMENT OF COGNITIVE AND NEURAL SYSTEMS BOSTON UNIVERSITY January 26 SELF--SIMILARITY IN NEURAL SIGNALS Professor Malvin Teich, Department of Electrical, Computer, and Systems Engineering, Boston University February 2 THE FUNCTIONAL ARCHITECTURE OF HUMAN VISUAL MOTION PERCEPTION Dr. Zhong-Lin Lu, Department of Cognitive Sciences and Institute for Mathematical Behavioral Sciences, University of California at Irvine February 9 DIVERSITY IN THE STRUCTURE AND FUNCTION OF HIPPOCAMPAL SYNAPSES Professor Kristen Harris, Division of Neuroscience, Children's Hospital and Program in Neuroscience, Harvard Medical School February 16 GROUP BEHAVIOR AND LEARNING IN AUTONOMOUS AGENTS Dr. Maja Mataric, Department of Computer Science, Brandeis University March 15 TOPOGRAPHY OF COGNITION: CELLULAR AND CIRCUIT BASIS OF WORKING MEMORY Dr. Patricia Goldman-Rakic, Neurobiology Section, Yale University School of Medicine March 22 EMOTION, MEMORY, AND THE BRAIN Professor Joseph LeDoux, Center for Neural Science, New York University April 5 AUDITORY PROCESSING OF COMPLEX SOUNDS Professor Laurel Carney, Department of Biomedical Engineering, Boston University April 19, 1:00--5:00 P.M. OPENING CELEBRATION FOR 677 BEACON STREET Invited lectures and refreshments to celebrate the new CNS building. Details to follow. Call 353-7857 for information. All talks except April 19 on Fridays at 2:00 PM in Room B02 (Please note the new lecture time!) Refreshments after the lecture in Room B01 677 Beacon Street, Boston From mav@psy.uq.oz.au Wed Jan 24 02:12:42 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 24 Jan 96 02:12:25 -0600; AA23946 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 24 Jan 96 02:12:23 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa21284; 22 Jan 96 23:23:13 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa21282; 22 Jan 96 23:08:22 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04152; 22 Jan 96 23:07:51 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa18291; 22 Jan 96 23:06:24 EST Received: from psych.psy.uq.oz.au by EDRC.CMU.EDU id aa26162; 22 Jan 96 23:05:55 EST Received: (from mav@localhost) by psych.psy.uq.edu.au (8.7.1/8.7.1) id OAA25783; Tue, 23 Jan 1996 14:04:45 +1000 (EST) Date: Tue, 23 Jan 1996 14:04:45 +1000 (EST) From: Simon Dennis To: connectionists@cs.cmu.edu Subject: Journal Launch: NOETICA, A Cognitive Science Forum Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII Welcome to NOETICA: A COGNITIVE SCIENCE FORUM We are pleased to announce the International launch of Noetica: A Cognitive Science Forum - a world wide web journal devoted to the interdisciplinary field of cognitive science. The journal is open for submissions and can be accessed using browsers such as Netscape, Mosaic and lynx at: http://psy.uq.edu.au/CogPsych/Noetica/ or alternatively you may access the mirror site at: http://www.cs.indiana.edu/Noetica/toc.html If you would like to subscribe to the cogpsy mailing list (which includes receiving a regular list of the new contents of Noetica) use the subscription form under "To Subscribe" on the home page or email us at noetica@psy.uq.edu.au. We would welcome any feedback you might have on the journal and look forward to providing a timely, lively, high quality forum for the discussion of cognitive science issues. Yours sincerely, Simon Dennis Cyril Latimer Kate Stevens Janet Wiles TABLE OF CONTENTS JOURNAL Volume 1 - 1995 Issue 1. The Impact of the Environment on the Word Frequency and Null List Strength Effects in Recognition Memory by Simon Dennis OPEN FORUM Volume 1 - 1995 The first three issues of volume one are papers which were presented at the Symposium on Connectionist Models and Psychology which took place in January, 1994 at the Department of Psychology, The University of Queensland, Australia. Issue 1: The rationale for psychologists using (connectionist) models Introduction: Peter Slezak. Target paper: Cyril Latimer. Computer Modelling of Cognitive Processes Invited Commentary: Max Coltheart. Connectionist Modelling and Cognitive Psychology Sally Andrews. What Connectionist Models Can (and Cannot) Tell Us George Oliphant. Connectionism, Psychology and Science Commentary: Paul Bakker. Good models of humble origins Richard Heath. Mathematical models, connectionism and cognitive processes Ellen Watson. Definitions and Interpretations: Comments on the symposium on connectionist models and psychology Issue 2. The correspondence between human and neural network performance Introduction: Cyril Latimer Review: Kate Stevens. The In(put)s and Out(put)s of Comparing Human and Network Performance: Some Ideas on Representations, Activations and Weights Review: Graeme Halford and William Wilson. How Far Do Neural Network Models Account for Human Reasoning? Commentary: Steven Phillips. Understanding as generalisation not just representation. Review: Simon Dennis. The Correspondence Between Psychological and Network Variables In Connectionist Models of Human Memory Commentaries: Andrew Heathcote. Connectionism: Implementation constraints for psychological models Phillip Sutcliffe. Contribution to discussion Issue 3. Computational processes over distributed memories Introduction: Steven Schwartz Review: Janet Wiles. The Connectionist Modeler's Toolkit: A review of some basic processes over distributed memories Invited Commentary: Mike Johnson. On the search for metaphors Zoltan Schreter. Distributed and Localist Representation in the Brain and in Connectionist Models Issue 4. The Sydney Morning Herald Word Database by Simon Dennis Issue 5. Introducing a new connectionist model: The spreading waves of activation network by Scott A. Gazzard ------------------------------------------------------------------------ Dr Simon Dennis Address: Department of Psychology Email: mav@psy.uq.edu.au The University of Queensland WWW: http://psy.uq.edu.au/~mav Brisbane, QLD, 4072, Australia From Jari.Kangas@hut.fi Wed Jan 24 14:54:24 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 24 Jan 96 14:54:21 -0600; AA08496 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 24 Jan 96 14:54:16 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa19851; 22 Jan 96 2:19:15 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa19849; 22 Jan 96 2:09:46 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa03256; 22 Jan 96 2:09:04 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id ab29143; 22 Jan 96 1:56:56 EST Received: from santra.hut.fi by CS.CMU.EDU id aa23941; 22 Jan 96 1:55:09 EST Received: from macula (macula.hut.fi [130.233.168.61]) by santra.hut.fi (8.7.3/8.7.3) with SMTP id IAA30065 for <@hut.fi:connectionists@cs.cmu.edu>; Mon, 22 Jan 1996 08:54:57 +0200 (EET) Received: from macula.hut.fi by macula via SMTP (950215.SGI.8.6.10/940406.SGI) for id IAA10380; Mon, 22 Jan 1996 08:54:57 +0200 Sender: jari@hut.fi Message-Id: <310334BB.167E@hut.fi> Date: Mon, 22 Jan 1996 08:54:51 +0200 From: Jari Kangas Organization: Helsinki University of Technology X-Mailer: Mozilla 2.0b4 (X11; I; IRIX 5.3 IP22) Mime-Version: 1.0 To: connectionists@cs.cmu.edu Subject: Location for SOM_PAK and LVQ_PAK has changed X-Url: http://nucleus.hut.fi/nnrc.html Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit Dear Neural Network Researchers, Out ftp-site cochlea.hut.fi containing the SOM_PAK and LVQ_PAK program packages has been off for a while because of hardware errors. We have now moved the public domain program packages to another location under our research centre www-page: http://nucleus.hut.fi/nnrc.html >From that page you will find a list entry pointing to a location where the packages are mirrored. The original location in cochlea.hut.fi is back in effect as soon as the machine is stable enough. Yours, Jari Kangas http://nucleus.hut.fi/~jari/ ------------------------------------------------------------------ ************************************************************************ * * * SOM_PAK * * * * The * * * * Self-Organizing Map * * * * Program Package * * * * Version 3.1 (April 7, 1995) * * * * Prepared by the * * SOM Programming Team of the * * Helsinki University of Technology * * Laboratory of Computer and Information Science * * Rakentajanaukio 2 C, SF-02150 Espoo * * FINLAND * * * * Copyright (c) 1992-1995 * * * ************************************************************************ Updated public-domain programs for Self-Organizing Map (SOM) algorithms are available via anonymous FTP on the Internet. From cia@kamo.riken.go.jp Wed Jan 24 14:54:33 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 24 Jan 96 14:54:29 -0600; AA08508 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 24 Jan 96 14:54:26 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa22179; 23 Jan 96 15:05:32 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa22162; 23 Jan 96 14:36:40 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04708; 23 Jan 96 14:36:03 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa23564; 23 Jan 96 6:45:38 EST Received: from kamo.riken.go.jp by EDRC.CMU.EDU id aa27355; 23 Jan 96 6:44:46 EST Received: by kamo.riken.go.jp; id AA14093; Tue, 23 Jan 1996 20:54:21 +0900 Message-Id: <9601231154.AA14093@kamo.riken.go.jp> To: connectionists@cs.cmu.edu Cc: cia@kamo.riken.go.jp Subject: New publications on blind signal processing Date: Tue, 23 Jan 96 20:54:21 +0900 From: cia@kamo.riken.go.jp X-Mts: smtp Dear Colleagues: Below please find a list of papers devoted to blind separation of sources presented at NOLTA-95 and NIPS . Some of these papers are available on the web site: http://www.bip.riken.go.jp/absl/absl.html I am preparing now extensive list of publications, reports and programs about blind signal processing (blind deconvolution, equalization, separation of sources, cocktail party-problem, blind identification and blind medium structure identification). Any information about new publications on these subjects are welcomed. Comments on our paper are also welcomed. Andrew Cichocki ------------------------------------------- Dr. A. Cichocki, Laboratory for Artificial Brain Systems, Frontier Research Program RIKEN, Institute of Physical and Chemical Research, Hirosawa 2-1, Saitama 351-01, WAKO-Schi, JAPAN E-mail: cia@kamo.riken.go.jp, URL: http://www.bip.riken.go.jp/absl/absl.html --------------------------------------------------- List of papers of Special Invited Session BLIND SEPARATION OF SOURCES- Information Processing in the Brain, NOLTA-95 , Las Vegas, USA, December 10-14, 1995. (Chair and organizer A. Cichocki) Proceedings 1995 International Symposium on Nonlinear Theory and Applications Vol.1: 1. Shun-ichi AMARI, Andrzej CICHOCKI and Howard Hua YANG, "RECURRENT NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES", pp.37-42. 2. Anthony J. BELL and Terrence J. SEJNOWSKI, "FAST BLIND SEPARATION BASED ON INFORMATION THEORY", pp. 43-47. 3. Adel BELOUCHRANI and Jean-Francois CARDOSO, "MAXIMUM LIKELIHOOD SOURCE SEPARATION BY THE EXPECTATION-MAXIMIZATION TECHNIQUE: DETERMINISTIC AND STOCHASTIC IMPLEMENTATION", pp.49-53. 4. Jean-Francois CARDOSO, "THE INVARIANT APPROACH TO SOURCE SEPARATION", pp. 55-60. 5. Andrzej CICHOCKI, Wlodzimierz KASPRZAK and Shun-ichi AMARI, "MULTI-LAYER NEURAL NETWORKS WITH LOCAL ADAPTIVE LEARNING RULES FOR BLIND SEPARATION OF SOURCE SIGNALS", pp.61-65. 6. Yannick DEVILLE and Laurence ANDRY, "APPLICATION OF BLIND SOURCE SEPARATION TECHNIQUES TO MULTI-TAG CONTACTLESS IDENTIFICATION SYSTEMS", pp. 73-78. 7. Jie HUANG , Noboru OHNISHI and Naboru SUGIE "SOUND SEPARATION BASED ON PERCEPTUAL GROUPING OF SOUND SEGMENTS", pp.67-72. 8. Christian JUTTEN and Jean-Francois CARDOSO, "SEPARATION OF SOURCES: REALLY BLIND ?" pp. 79-84. 9. Kiyotoshi MATSUOKA and Mitsuru KAWAMOTO, "BLIND SIGNAL SEPARATION BASED ON A MUTUAL INFORMATION CRITERION", pp. 85-91. 10. Lieven De LATHAUWER, Pierre COMON, Bart De MOOR and Joos VANDEWALLE, "HIGHER-ORDER POWER METHOD - APPLICATION IN INDEPENDENT COMPONENT ANALYSIS", pp. 91-96. 11. Jie ZHU, Xi-Ren CAO, and Ruey-Wen LIU, "BLIND SOURCE SEPARATION BASED ON OUTPUT INDEPENDENCE - THEORY AND IMPLEMENTATION", pp. 97-102. ---------------------------------------------------------------------------- Selected list of recent publications and reports about ICA [1] S. Amari, A. Cichocki and H. H. Yang, "A new learning algorithm for blind signal separation", NIPS-95, Denver Dec. 1995, vol.8, MIT Press, 1996 (in print). [2] S. Amari, A. Cichocki and H. H. Yang, "Recurrent neural networks for blind separation of sources", Nolta-95 , Las Vegas, Dec.10-15, 1995, vol.1, pp. 37-42. [3] A.Cichocki and L. Moszczynski, "A new learning algorithm for for blind separation of sources", Electronics Letters, vol.28, No.21,1992, pp.1986-1987. [4] A. Cichocki, R. Unbehauen and E. Rummert, Robust learning algorithm for blind separation of signals", Electronics Letters, vol.30, No.17, 18th August 1994, pp.1386-1387. [5] A. Cichocki, R. Unbehauen, L. Moszczynski and E. Rummert, "A new on-line adaptive algorithm for blind separation of source signals", 1994 Int. Symposium on Artificial Neural Networks ISANN-94, Tainan, Taiwan , Dec.1994, pp.406-411. [6] A. Cichocki, R. Bogner, L. Moszczynski, Improved adaptive algorithms for blind separation of sources", Proc. of Conference on Electronic Circuits and Systems, KKTOiUE, Zakopane Poland, Oct. 25-27, 1995, pp. 647-652. [7] A. Cichocki, R. Unbehauen, "Robust neural networks with on-line learning for blind identification and blind separation of sources", submitted for publication to IEEE Transaction on Circuits and Systems (submitted June 1994). [8] A.Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal Processing, John Wiley 1994 (new revised and improved edition), pp. 461-471. [9] A. Cichocki, W. Kasprzak, S. Amari, "Multi-layer neural networks with a local adaptive learning rule for blind separation of source signals", Nolta-95, Las Vegas, Dec.10-15, 1995, vol.1 pp. 61-66. [10] A. Cichocki, S. Amari, M. Adachi and W. Kasprzak, "Self-adaptive neural networks for blind separation of sources", ISCAS-96 May 1996, Atlanta, USA. --------------------------------------------------------------- From S.Goonatilake@cs.ucl.ac.uk Wed Jan 24 14:54:38 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 24 Jan 96 14:54:34 -0600; AA08517 Message-Id: <9601242054.AA08559@lucy.cs.wisc.edu> Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 24 Jan 96 14:54:30 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab22179; 23 Jan 96 15:07:41 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa22167; 23 Jan 96 14:37:32 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04716; 23 Jan 96 14:37:26 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa26997; 23 Jan 96 12:33:09 EST Received: from bells.cs.ucl.ac.uk by CS.CMU.EDU id aa09074; 23 Jan 96 12:32:06 EST Received: from stanley.cs.ucl.ac.uk by bells.cs.ucl.ac.uk with local SMTP id ; Tue, 23 Jan 1996 17:30:25 +0000 To: connectionists@cs.cmu.edu Subject: New Book - Intelligent Systems for Finance and Business Date: Tue, 23 Jan 96 17:30:48 +0000 From: Suran Goonatilake NEW BOOK ANNOUNCEMENT INTELLIGENT SYSTEMS FOR FINANCE AND BUSINESS Suran Goonatilake and Philip Treleaven (Eds.) University College London Intelligent Systems are now beginning to be successfully applied in a variety of financial and business modelling tasks. These methods which include genetic algorithms, neural networks, fuzzy systems and intelligent hybrid systems are now being applied in credit evaluation, direct marketing, fraud detection, securities trading and portfolio management, and in many cases are outperforming traditional approaches. This book brings together leading professionals from the US, Europe and Asia who have developed intelligent systems to tackle some of the most challenging problems in finance and business. It covers applications of a large number of intelligent techniques: genetic algorithms, neural networks, fuzzy logic, expert systems, rule induction, genetic programming, case based reasoning and intelligent hybrid systems. Case studies are drawn from a wide variety of business sectors. Applications that are detailed include: credit evaluation, direct marketing, insider dealing detection, insurance fraud detection, insurance claims processing, financial trading, portfolio management, and economic modelling. CONTENTS ======== Foreword: Cathy Basch, Visa International Chapter 1: Intelligent Systems for Finance and Business: An Overview Suran Goonatilake, University College London, UK. PART ONE: CREDIT SERVICES Chapter 2: Intelligent Systems at American Express Robert Didner, American Express Chapter 3: Credit Evaluation using a Genetic Algorithm R. Walker, E.W. Haasdijk and M.C. Gerrets, CAP-Volmac. Chapter 4: Neural Networks for Credit Scoring David Leigh PART TWO: DIRECT MARKETING Chapter 5: Neural Networks for Data Driven Marketing Peter Furness, AMS Management Systems Chapter 6:Intelligent Systems for Market Segmentation and Local Market Planning Richard Webber, CCN Marketing PART THREE: FRAUD DETECTION AND INSURANCE Chapter 7: A Fuzzy System for Detecting Anomalous Behaviors in Healthcare Provider Claims Earl Cox, Metus Systems. Chapter 8: Insider Dealing Detection at the Toronto Stock Exchange Steve Mott, Cognitive Systems Chapter 9: EFD: Heuristic Statistics for Insurance Fraud Detection J.A. Major and D.R. Riedinger, Travelers Insurance Co Chapter 10: Expert Systems at Lloyd's of London Colin Talbot, Lloyd's of London PART FOUR: SECURITIES TRADING AND PORTFOLIO MANAGEMENT Chapter 11: Neural Networks in Investment Management A. N. Refenes, A. D. Zapranis, J.T. Connor and D.W. Bunn London Business School Chapter 12: Fuzzy Logic for financial trading Shunichi Tano, Hitachi Labs.. Chapter 13: Syntactic Pattern-Based Inductive Learning for Chart Analysis Jae K. Lee, Hyun Soo Kim, KAIST. PART FIVE: ECONOMIC MODELLING Chapter 14: Genetic Programming for Economic Modelling John Koza, Stanford University Chapter 15: Modelling artificial stock markets using Genetic Algorithms Paul Tayler, Brunel University Chapter 16: Intelligent, Self Organising Models in Economics and Finance Peter Allen, Cranfield Institute of Technology PART SIX: IMPLEMENTING INTELLIGENT SYSTEMS Chapter 17: Software for Intelligent Systems Philip Treleaven, University College London ------------------------------------------------------------------ ISBN : 0471 94404 1 Publication Date : December 1995 Price: $55, (Sterling) 40 Publishers: (US) John Wiley & Sons Inc., 605 Third Avenue, New York, NY 10158-0012 Tel: 1-800-225-5945 (UK) John Wiley & Sons Ltd, Baffins Lane, Chichester, West Sussex, PO19 1UD, UK. Tel: 0800 243 407 ------------------------------------------------------------------- A World Wide Web page at : http://www.cs.ucl.ac.uk/staff/S.Goonatilake/busbook.html From cia@kamo.riken.go.jp Wed Jan 24 14:54:57 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 24 Jan 96 14:54:54 -0600; AA08534 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 24 Jan 96 14:54:49 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa22598; 23 Jan 96 22:05:36 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa22594; 23 Jan 96 21:50:22 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04862; 23 Jan 96 21:49:53 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa04340; 23 Jan 96 21:10:03 EST Received: from [134.160.48.10] by RI.CMU.EDU id aa02440; 23 Jan 96 21:09:01 EST Received: by kamo.riken.go.jp; id AA14489; Wed, 24 Jan 1996 11:18:35 +0900 Message-Id: <9601240218.AA14489@kamo.riken.go.jp> To: neuronet@tutkie.tut.ac.jp, connectionists@cs.cmu.edu Cc: cia@kamo.riken.go.jp Subject: Blind Signal Processing - Call for Paper Date: Wed, 24 Jan 96 11:18:35 +0900 From: cia@kamo.riken.go.jp X-Mts: smtp Call for papers in special Invited Session in ICONIP96, Hong Kong: BLIND SIGNAL PROCESSING - ADAPTIVE AND NEURAL NETWORK APPROACHES I would like to announce that I am organizing Special Invited Session in ICONIP-96 (September 24-27, 1996,Hong-Kong) devoted to blind signal processing using neural and adaptive approaches. Papers devoted to all aspects of blind signal processing: blind deconvolution, equalization, separation of sources, blind identification, blind medium structure identification, cocktail party-problem, applications to EEG and ECG, voice enhancement and recognition, etc. are welcomed. Authors are invited to submit by e-mail (to me) as soon as possible,but not latter than February 15, extended summary (2-3 pages) or full paper. The final camera ready paper should be submitted not latter than March 1, 1996. Andrew Cichocki --------------------------- Dr. A. Cichocki, Laboratory for Artificial Brain Systems, Frontier Research Program RIKEN, Institute of Physical and Chemical Research, Hirosawa 2-1, Saitama 351-01, WAKO-Schi, JAPAN E-mail: cia@kamo.riken.go.jp, FAX (+81) 48 462 4633. URL: http://www.bip.riken.go.jp/absl/absl.html From john@dcs.rhbnc.ac.uk Wed Jan 24 14:55:28 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 24 Jan 96 14:54:49 -0600; AA08532 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 24 Jan 96 14:54:34 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ac22179; 23 Jan 96 15:10:00 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa22173; 23 Jan 96 14:47:02 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04726; 23 Jan 96 14:46:14 EST From: John Shawe-Taylor Message-Id: <199601231535.PAA06083@platon.cs.rhbnc.ac.uk> To: Connectionists@cs.cmu.edu Subject: Technical Report Series in Neural and Computational Learning Date: Tue, 23 Jan 96 15:35:55 +0000 The European Community ESPRIT Working Group in Neural and Computational Learning Theory (NeuroCOLT) has produced a set of new Technical Reports available from the remote ftp site described below. They cover topics in real valued complexity theory, computational learning theory, and analysis of the computational power of continuous neural networks. Abstracts are included for most of the titles. *** Please note that the location of the files has been changed so that *** any copies you have of the previous instructions should be discarded. *** The new location and instructions are given at the end of the list. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-001: ---------------------------------------- On digital nondeterminism by Felipe Cucker, Universitat Pompeu Fabra, Spain Martin Matamala, Universidad de Chile, Chile No abstract available. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-002: ---------------------------------------- Complexity and Real Computation: A Manifesto by Lenore Blum, International Computer Science Institute, Berkeley, USA Felipe Cucker, Universitat Pompeu Fabra, Spain Mike Shub, IBM T.J. Watson Research Center, New York, USA Steve Smale, University of California, USA Abstract: Finding a natural meeting ground between the highly developed complexity theory of computer science -- with its historical roots in logic and the discrete mathematics of the integers -- and the traditional domain of real computation, the more eclectic less foundational field of numerical analysis -- with its rich history and longstanding traditions in the continuous mathematics of analysis -- presents a compelling challenge. Here we illustrate the issues and pose our perspective toward resolution. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-003: ---------------------------------------- Models for Parallel Computation with Real Numbers by F. Cucker, Universitat Pompeu Fabra, Spain J.L. Montana, Universidad de Cantabria, Spain L.M. Pardo, Universidad de Cantabria, Spain Abstract: This paper deals with two models for parallel computations over the reals. On the one hand, a generalization of the real Turing machine obtained by assembling a polynomial number of such machines that work together in polylogarithmic time (more or less like a PRAM in the Boolean setting) and, on the other hand, a model consisting of families of algebraic circuits generated in some uniform way. We show that the classes defined by these two models are related by a chain of inclusions and that some of these inclusions are strict. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-004: ---------------------------------------- Nash Trees and Nash Complexity by Felipe Cucker, Universitat Pompeu Fabra, Spain Thomas Lickteig, Universit\"at Bonn, Germany Abstract: Numerical analysis computational problems such as Cholesky decomposition of a positive definite matrix, or unitary transformation of a complex matrix into upper triangular form (for instance by the Householder algorithm), require algorithms that use also ``non-arithmetical'' operations such as square roots. The aim of this paper is twofold: 1. Generalizing the notions of arithmetical semi-algebraic decision trees and computation trees (that is, with outputs) we suggest a definition of Nash trees and Nash straight line programs (SLPs), necessary to formalize and analyse numerical analysis algorithms and their complexity as mentioned above. These trees and SLPs have a Nash operational signature $N^R$ over a real closed field $R$. Based on the sheaf of abstract Nash functions over the real spectrum of a ring as introduced by M.-F. Roy, we propose a category nash_R of partial (homogeneous) N^R-algebras in which these Nash operations make sense in a natural way. 2. Using this framework, in particular the execution of $N^R$-SLPs in appropriate $N^R$-algebras, we extend the degree-gradient lower bound to Nash decision complexity of the membership problem of co-one-dimensional semi-algebraic subsets of open semi-algebraic subsets. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-005: ---------------------------------------- On the computational power and super-Turing capabilities of dynamical systems by Olivier Bournez, Department LIP, ENS-Lyon, France Michel Cosnard, Department LIP, ENS-Lyon, France Abstract: We explore the simulation and computational capabilities of dynamical systems. We first introduce and compare several notions of simulation between discrete systems. We give a general framework that allows dynamical systems to be considered as computational machines. We introduce a new discrete model of computation: the analog automaton model. We determine the computational power of this model and prove that it does have super-Turing capabilities. We then prove that many very simple dynamical systems from the literature are actually able to simulate analog automata. From this result we deduce that many dynamical systems have intrinsically super-Turing capabilities. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-006: ---------------------------------------- Finite Sample Size Results for Robust Model Selection; Application to Neural Networks by Joel Ratsaby, Technion, Israel Ronny Meir, Technion, Israel Abstract: The problem of model selection in the face of finite sample size is considered within the framework of statistical decision theory. Focusing on the special case of regression, we introduce a model selection criterion which is shown to be robust in the sense that, with high confidence, even for a finite sample size it selects the best model. Our derivation is based on uniform convergence methods, augmented by results from the theory of function approximation, which permit us to make definite probabilistic statements about the finite sample behavior. These results stand in contrast to classical approaches, which can only guarantee the asymptotic optimality of the choice. The criterion is demonstrated for the problem of model selection in feedforward neural networks. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-007: ---------------------------------------- On the structure of $\npoly{C}$ by Gregorio Malajovich, Klaus Meer, RWTH Aachen, Germany Abstract: This paper deals with complexity classes $\poly{C}$ and $\npoly{C}$, as they were introduced over the complex numbers by Blum, Shub and Smale. Under the assumption $\poly{C} \ne \npoly{C}$ the existence of non-complete problems in $\npoly{C}$~, not belonging to $\poly{C}$~, is established. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-008: ---------------------------------------- Dynamic Recurrent Neural Networks: a Dynamical Analysis by Jean-Philippe DRAYE, Davor PAVISIC, Facult\'{e} Polytechnique de Mons, Belgium, Guy CHERON, Ga\"{e}tan LIBERT, University of Brussels, Belgium Abstract: In this paper, we explore the dynamical features of a neural network model which presents two types of adaptative parameters~: the classical weights between the units and the time constants associated with each artificial neuron. The purpose of this study is to provide a strong theoretical basis for modeling and simulating dynamic recurrent neural networks. In order to achieve this, we study the effect of the statistical distribution of the weights and of the time constants on the network dynamics and we make a sta tistical analysis of the neural transformation. We examine the network power spectra (to draw some conclusions over the frequent ial behavior of the network) and we compute the stability regions to explore the stability of the model. We show that the network is sensitive to the variations of the mean values of th e weights and the time constants (because of the temporal aspects of the learned tasks). Nevertheless, our results highlight the improvements in the network dynamics due to the introduction of adaptative time constants and indicate that dynamic recu rrent neural networks can bring new powerful features in the field of neural computing. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-009: ---------------------------------------- Scale-sensitive Dimensions, Uniform Convergence, and Learnability by Noga Alon, Tel Aviv University (ISRAEL), Shai Ben-David, Technion, (ISRAEL), Nicol\`o Cesa-Bianchi, DSI, Universit\`a di Milano, David Haussler, UC Santa Cruz, (USA) Abstract: Learnability in Valiant's PAC learning model has been shown to be strongly related to the existence of uniform laws of large numbers. These laws define a distribution-free convergence property of means to expectations uniformly over classes of random variables. Classes of real-valued functions enjoying such a property are also known as uniform Glivenko-Cantelli classes. In this paper we prove, through a generalization of Sauer's lemma that may be interesting in its own right, a new characterization of uniform Glivenko-Cantelli classes. Our characterization yields Dudley, Gin\'e, and Zinn's previous characterization as a corollary. Furthermore, it is the first based on a simple combinatorial quantity generalizing the Vapnik-Chervonenkis dimension. We apply this result to obtain the weakest combinatorial condition known to imply PAC learnability in the statistical regression (or ``agnostic'') framework. Furthermore, we show a characterization of learnability in the probabilistic concept model, solving an open problem posed by Kearns and Schapire. These results show that the accuracy parameter plays a crucial role in determining the effective complexity of the learner's hypothesis class. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-010: ---------------------------------------- On-line Prediction and Conversion Strategies by Nicol\`o Cesa-Bianchi, DSI, Universit\`a di Milano, Yoav Freund, AT\&T Bell Laboratories, David P.\ Helmbold, University of California, Santa Cruz, Manfred K.\ Warmuth, University of California, Santa Cruz Abstract: We study the problem of deterministically predicting boolean values by combining the boolean predictions of several experts. Previous on-line algorithms for this problem predict with the weighted majority of the experts' predictions. These algorithms give each expert an exponential weight $\beta^m$ where $\beta$ is a constant in $[0,1)$ and $m$ is the number of mistakes made by the expert in the past. We show that it is better to use sums of binomials as weights. In particular, we present a deterministic algorithm using binomial weights that has a better worst case mistake bound than the best deterministic algorithm using exponential weights. The binomial weights naturally arise from a version space argument. We also show how both exponential and binomial weighting schemes can be used to make prediction algorithms robust against noise. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-011: ---------------------------------------- Worst-case Quadratic Loss Bounds for Prediction Using Linear Functions and Gradient Descent by Nicol\`o Cesa-Bianchi, DSI, Universit\`a di Milano, Philip M. Long, Duke University, Manfred K. Warmuth, UC Santa Cruz Abstract: In this paper we study the performance of gradient descent when applied to the problem of on-line linear prediction in arbitrary inner product spaces. We show worst-case bounds on the sum of the squared prediction errors under various assumptions concerning the amount of {\it a priori} information about the sequence to predict. The algorithms we use are variants and extensions of on-line gradient descent. Whereas our algorithms always predict using linear functions as hypotheses, none of our results requires the data to be linearly related. In fact, the bounds proved on the total prediction loss are typically expressed as a function of the total loss of the best fixed linear predictor with bounded norm. All the upper bounds are tight to within constants. Matching lower bounds are provided in some cases. Finally, we apply our results to the problem of on-line prediction for classes of smooth functions. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-012: ---------------------------------------- Using Bayesian Methods for Avoiding Overfitting and for Ranking Networks in Multilayer Perceptrons Learning by Michel de Bollivier, EC Joint Research Centre, Italy, Domenico Perrotta, EC Joint Research Centre and Ecole Normale Sup\'{e}rieure de Lyon, France Abstract: This work is an experimental attempt to determine whether the Bayesian paradigm could improve Multi-Layer Perceptrons (MLPs) learning methods. In particular, we exper iment here the paradigm developed by D. MacKay (1992). The paper points out the main or critical points of MacKay's work and introduces very practical points of Bayesian MLPs, having in mind future applications. Then, Bayesian MLPs are used on three public classification databases and compar ed to other methods. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-013: ---------------------------------------- Lower Bounds for the Computational Power of Networks of Spiking Neurons by Wolfgang Maass, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Abstract: We investigate the computational power of a formal model for networks of spiking neurons. It is shown that simple operations on phase-differences between spike-trains provide a very powerful computational tool that can in principle be used to carry out highly complex computations on a small network of spiking neurons. We construct networks of spiking neurons that simulate arbitrary threshold circuits, Turing machines, and a certain type of random access machines with real valued inputs. We also show that relatively weak basic assumptions about the response- and threshold-functions of the spiking neurons are sufficient in order to employ them for such computations. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-014: ---------------------------------------- Analog Computations on Networks of Spiking Neurons by Wolfgang Maass, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Abstract: We characterize the class of functions with real-valued input and output which can be computed by networks of spiking neurons with piecewise linear response- and threshold-functions and unlimited timing precision. We show that this class coincides with the class of functions computable by recurrent analog neural nets with piecewise linear activation functions, and with the class of functions computable on a certain type of random access machine (N-RAM) which we introduce in this article. This result is proven via constructive real-time simulations. Hence it provides in particular a convenient method for constructing networks of spiking neurons that compute a given real-valued function $f$: it now suffices to write a program for constructing networks of spiking neurons that compute a given real-valued function $f$: it now suffices to write a program for computing $f$ on an N-RAM; that program can be ``automatically'' transformed into an equivalent network of spiking neurons (by our simulation result). Finally, one learns from the results of this paper that certain very simple piecewise linear response- and threshold-functions for spiking neurons are {\it universal}, in the sense that neurons with these particular response- and threshold-functions can simulate networks of spiking neurons with {\it arbitrary} piecewise linear response- and threshold-functions. The results of this paper also show that certain very simple piecewise linear activation functions are in a corresponding sense universal for recurrent analog neural nets. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-015: ---------------------------------------- Vapnik-Chervonenkis Dimension of Neural Nets by Wolfgang Maass, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Abstract: We will survey in this article the most important known bounds for the VC-dimension of neural nets that consist of linear threshold gates (section 2) and for the case of neural nets with real-valued activation functions (section 3). In section 4 we discuss a generalization of the VC-dimension for neural nets with non-boolean network-output. With regard to a discussion of the VC-dimension of models for networks of {\it spiking neurons} we refer to Maass (1994). ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-016: ---------------------------------------- On the Computational Power of Noisy Spiking Neurons by Wolfgang Maass, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Abstract: This article provides some first results about the computational power of neural networks that are based on a neuron model which is acceptable to many neurobiologists as being reasonably realistic for a biological neuron. Biological neurons communicate via spike-trains, i.e. via sequences of stereotyped pulses (``spikes'') that encode information in their time-differences (``temporal coding''). In addition it is wellknown that biological neurons are quite ``noisy'', i.e. the precise times when they ``fire'' (and thereby issue a spike) depend not only on the incoming spike-trains, but also on various types of ``noise''. It has remained unknown whether one can in principle carry out reliable digital computations with noisy spiking neurons. This article presents rigorous constructions for simulating in real-time arbitrary given boolean circuits and finite automata with arbitrarily high reliability by networks of noisy spiking neurons. In addition we show that with the help of ``shunting inhibition'' such networks can simulate in real-time any McCulloch-Pitts neuron (or ``threshold gate''), and therefore any multilayer perceptron (or ``threshold circuit'') in a reliable manner. These constructions provide a possible explanation for the fact that biological neural systems can carry out quite complex computations within 100 msec. It turns out that the assumption that these constructions require about the shape of the EPSP's and the behaviour of the noise are surprisingly weak. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-017: ---------------------------------------- Die Komplexit\"at des Rechnens und Lernens mit neuronalen Netzen -- Ein Kurzf\"uhrer by Michael Schmitt, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Abstract: This is a very short guide to the basic concepts of the theory of computing and learning with neural networks with emphasis on computational complexity. Fundamental results on circuit complexity of neural networks and PAC-learning are mentioned but no proofs are given. A list of references to the most important and most recent books in the field is included. The report was written in German on the occasion of giving a course at the Autumn School in Connectionism and Neural Networks ``HeKoNN 95'' in M\"unster. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-018: ---------------------------------------- Tracking the best disjunction by Peter Auer, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Manfred Warmuth, University of California at Santa Cruz, USA Abstract: Littlestone developed a simple deterministic on-line learning algorithm for learning $k$-literal disjunctions. This algorithm (called Winnow) keeps one weight for each of the $n$ variables and does multiplicative updates to its weights. We develop a randomized version of Winnow and prove bounds for an adaptation of the algorithm for the case when the disjunction may change over time. In this case a possible target {\em disjunction schedule} $\Tau$ is a sequence of disjunctions (one per trial) and the {\em shift size} is the total number of literals that are added/removed from the disjunctions as one progresses through the sequence. We develop an algorithm that predicts nearly as well as the best disjunction schedule for an arbitrary sequence of examples. This algorithm that allows us to track the predictions of the best disjunction is hardly more complex than the original version. However the amortized analysis needed for obtaining worst-case mistake bounds requires new techniques. In some cases our lower bounds show that the upper bounds of our algorithm have the right constant in front of the leading term in the mistake bound and almost the right constant in front of the second leading term. By combining the tracking capability with existing applications of Winnow we are able to enhance these applications to the shifting case as well. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-019: ---------------------------------------- Learning Nested Differences in the Presence of Malicious Noise by Peter Auer, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Abstract: We investigate the learnability of nested differences of intersection-closed classes in the presence of malicious noise. Examples of intersection-closed classes include axis-parallel rectangles, monomials, linear sub-spaces, and so forth. We present an on-line algorithm whose mistake bound is optimal in the sense that there are concept classes for which each learning algorithm (using nested differences as hypotheses) can be forced to make at least that many mistakes. We also present an algorithm for learning in the PAC model with malicious noise. Surprisingly enough, the noise rate tolerable by these algorithms does not depend on the complexity of the target class but depends only on the complexity of the underlying intersection-closed class. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-020: ---------------------------------------- Characterizing the Learnability of Kolmogorov Easy Circuit Expressions by Jos\'e L. Balc\'azar, Universitat Polit\'ecnica de Catalunya, Spain Harry Buhrman, Centrum voor Wiskunde en Informatica, the Netherlands Abstract: We show that Kolmogorov easy circuit expressions can be learned with membership queries in polynomial time if and only if every NE-predicate is E-solvable. Moreover we show that the previously known algorithm, that uses an oracle in NP, is optimal in some relativized world. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-021: ---------------------------------------- T2 - Computing optimal 2-level decision tree by Peter Auer, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria *** Note: This is a C program available in tarred (compressed) format. Description: This is a short description of the T2 program discussed in P. Auer, R.C. Holte, and W. Maass. Theory and applications of agnostic PAC-learning with small decision trees. In Proc. 7th Int. Machine Learning Conf., Tahoe City (USA), 1995. Please see the paper for a description of the algorithm and a discussion of the results. (There is a typo in the paper in Table 2: The Sky2 value for HE is 89.0% instead of 91.0%.) T2 calculates optimal decision trees up to depth 2. T2 accepts exactly the same input as C4.5, consisting of a name-file, a data-file, and an optional test-file. The output of TREE2 is a decision tree similar to the decision trees of C4.5, but there are some differences. T2 uses two kinds of decision nodes: (1) discrete splits on an discrete attribute where the node has as many branches as there are possible attribute values, and (2) interval splits of continuous attributes. A node which performs an interval split divides the real line into intervals and has as many branches as there are intervals. The number of intervals is restricted to be (a) at most MAXINTERVALS if all the branches of the decision node lead to leaves, and to be (b) at most 2 otherwise. MAXINTERVALS can be set by the user. The attribute value ``unknown'' is treated as a special attribute value. Each decision node (discrete or continuous) has an additional branch which takes care of unknown attribute values. T2 builds the decision tree satisfying the above constraints and minimizing the number of misclassifications of cases in the data-file. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-022: ---------------------------------------- Efficient Learning with Virtual Threshold Gates by Wolfgang Maass, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Manfred Warmuth, University of California, Santa Cruz, USA Abstract: We reduce learning simple geometric concept classes to learning disjunctions over exponentially many variables. We then apply an on-line algorithm called Winnow whose number of prediction mistakes grows only logarithmically with the number of variables. The hypotheses of Winnow are linear threshold functions with one weight per variable. We find ways to keep the exponentially many weights of Winnow implicitly so that the time for the algorithm to compute a prediction and update its ``virtual'' weights is polynomial. Our method can be used to learn $d$-dimensional axis-parallel boxes when $d$ is variable, and unions of $d$-dimensional axis-parallel boxes when $d$ is constant. The worst-case number of mistakes of our algorithms for the above classes is optimal to within a constant factor, and our algorithms inherit the noise robustness of Winnow. We think that other on-line algorithms with multiplicative weight updates whose loss bounds grow logarithmically with the dimension are amenable to our methods. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-023: ---------------------------------------- On learnability and predicate logic (Extended Abstract) by Wolfgang Maass, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Gy. Tur\'{a}n, University of Illinois at Chicago, USA No abstract available. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-024: ---------------------------------------- Lower Bounds on Identification Criteria for Perceptron-like Learning Rules by Michael Schmitt, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Abstract: Topic of this paper is the computational complexity of identifying neural weights using Perceptron-like learning rules. By Perceptron-like rules we understand instructions to modify weight vectors by adding or subtracting constant values after occurrence of an error. By computational complexity we mean worst-case bounds on the number of correction steps. The training examples are taken from Boolean functions computable by McCulloch-Pitts neurons. Exact identification by the Perceptron rule is known to take exponential time in the worst case. Therefore, we define identification criteria that do not require that the learning process exactly identifies the function being learned: PAC identification, order identification, and sign identification. Our results show that Perceptron-like learning rules cannot satisfy any of these criteria when the number of correction steps is to be bounded by a polynomial. This indicates that even by considerably lowering one's demands on the learning process one cannot prevent Perceptron rules from being computationally infeasible. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-025: ---------------------------------------- On Methods to Keep Learning Away from Intractability (Extended abstract) by Michael Schmitt, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Abstract: We investigate the complexity of learning from restricted sets of training examples. With the intention to make learning easier we introduce two types of restrictions that describe the permitted training examples. The strength of the restrictions can be tuned by choosing specific parameters. We ask how strictly their values must be limited to turn NP-complete learning problems into polynomial-time solvable ones. Results are presented for Perceptrons with binary and arbitrary weights. We show that there exist bounds for the parameters that sharply separate efficiently solvable from intractable learning problems. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-026: ---------------------------------------- Accuracy of techniques for the logical analysis of data by Martin Anthony, London School of Economics, UK Abstract: We analyse the generalisation accuracy of standard techniques for the `logical analysis of data', within a probabilistic framework. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-027: ---------------------------------------- Interpolation and Learning in Artificial Neural Networks by Martin Anthony, London School of Economics, UK No abstract available. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-028: ---------------------------------------- Threshold Functions, Decision Lists, and the Representation of Boolean Functions by Martin Anthony, London School of Economics, UK Abstract: We describe a geometrically-motivated technique for data classification. Given a finite set of points in Euclidean space, each classified according to some target classification, we use a hyperplane to separate off a set of points all having the same classification; these points are then deleted from the database and the procedure is iterated until no points remain. We explain how such an iterative `chopping procedure' leads to a type of decision list classification of the data points and in a classification of the data by means of a linear threshold artificial neural network with one hidden layer. In the case where the data points are all the $2^n$ vertices of the Boolean hypercube, the technique produces a neural network representation of Boolean functions differing from the obvious one based on a function's disjunctive normal formula. ---------------------------------------- NeuroCOLT Technical Report NC-TR-96-029: ---------------------------------------- Learning of Depth Two Neural Nets with Constant Fan-in at the Hidden Nodes by Peter Auer, University of California, Santa Cruz, USA, Stephen Kwek, University of Illinois, USA, Wolfgang Maass, Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria Manfred K. Warmuth, University of California, Santa Cruz, USA Abstract: We present algorithms for learning depth two neural networks where the hidden nodes are threshold gates with constant fan-in. The transfer function of the output node might be more general: in addition to the threshold function we have results for the logistic and the linear transfer function at the output node. We give batch and on-line learning algorithms for these classes of neural networks and prove bounds on the performance of our algorithms. The batch algorithms work for real valued inputs whereas the on-line algorithms require that the inputs are discretized. The hypotheses of our algorithms are essentially also neural networks of depth two. However, their number of hidden nodes might be much larger than the number of hidden nodes of the neural network that has to be learned. Our algorithms can handle a large number of hidden nodes since they rely on multiplicative weight updates at the output node, and the performance of these algorithms scales only logarithmically with the number of hidden nodes used. -------------------------------------------------------------------- ***************** ACCESS INSTRUCTIONS ****************** The Report NC-TR-96-001 can be accessed and printed as follows % ftp ftp.dcs.rhbnc.ac.uk (134.219.96.1) Name: anonymous password: your full email address ftp> cd pub/neurocolt/tech_reports ftp> binary ftp> get nc-tr-96-001.ps.Z ftp> bye % zcat nc-tr-96-001.ps.Z | lpr -l Similarly for the other technical reports. Uncompressed versions of the postscript files have also been left for anyone not having an uncompress facility. In some cases there are two files available, for example, nc-tr-96-002-title.ps.Z nc-tr-96-002-body.ps.Z The first contains the title page while the second contains the body of the report. The single command, ftp> mget nc-tr-96-002* will prompt you for the files you require. A full list of the currently available Technical Reports in the Series is held in a file `abstracts' in the same directory. The files may also be accessed via WWW starting from the NeuroCOLT homepage (note that this is undergoing some corrections and may be temporarily inaccessible): http://www.dcs.rhbnc.ac.uk/neural/neurocolt.html Best wishes John Shawe-Taylor From tgc@kcl.ac.uk Fri Jan 26 10:03:39 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 26 Jan 96 10:03:23 -0600; AA28427 Message-Id: <9601261603.AA19015@lucy.cs.wisc.edu> Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 26 Jan 96 10:03:20 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa22156; 23 Jan 96 14:53:38 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa22154; 23 Jan 96 14:35:00 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04701; 23 Jan 96 14:34:20 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa21891; 23 Jan 96 5:17:06 EST Received: from [137.73.66.10] by EDRC.CMU.EDU id aa27235; 23 Jan 96 5:16:22 EST Received: from bay.cc.kcl.ac.uk by cayman with SMTP (PP); Tue, 23 Jan 1996 10:14:30 +0000 Received: from remote194.dialup.kcl.ac.uk by alder.cc.kcl.ac.uk (MX V4.2 AXP) with SMTP; Tue, 23 Jan 1996 10:11:35 GMT X-Sender: udee218@alder.cc.kcl.ac.uk (Unverified) X-Mailer: Windows Eudora Version 1.4.4 Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Tue, 23 Jan 1996 10:15:55 +0000 To: connectionists@cs.cmu.edu From: Trevor Clarkson Subject: NEuroFuzzy Workshop in Prague, 16-18 April 1996 Cc: A limited number of studentships of 450 ECU are still available from the NEuroNet programme for EU students only to attend the NEuroFuzzy workshop. The grant is a contribution to travel, accommodation and registration so that students will be able to participate in the technical sessions as well as the tutorials. The first set of studentships have been approved and letters have been sent to successful applicants. The remaining studentships will be awarded on a first-come first-served basis to students who are registered full-time for a university degree. Applicants should send a short (half-page) biography which clearly states age, European nationality and place of study. This must be accompanied by a letter of support from their head of department confirming these details. For details concerning these studentships only, contact the NEuroNet office: Ms Terhi Garner, NEuroNet Department of Electronic and Electrical Engineering, King's College London Strand, London WC2R 2LS Email: terhi.garner@kcl.ac.uk Fax: +44 171 873 2559 __________________________________________________________________________ Professor Trevor Clarkson * * Director, NEuroNet (European Network of Excellence in Neural Networks) * Department of Electronic and Electrical Engineering * * King's College London * * Strand, London WC2R 2LS, UK * * * * Tel: +44 171 873 2367/2388 Fax: +44 171 873 2559 WWW: http://www.neuronet.ph.kcl.ac.uk/ Email: tgc@kcl.ac.uk __________________________________________________________________________ From georg@ai.univie.ac.at Fri Jan 26 10:03:49 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 26 Jan 96 10:03:38 -0600; AA28441 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 26 Jan 96 10:03:35 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab23613; 24 Jan 96 15:28:13 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab23611; 24 Jan 96 15:03:59 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa00930; 24 Jan 96 15:03:48 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa08485; 24 Jan 96 5:12:54 EST Received: from jedlesee.ai.univie.ac.at by RI.CMU.EDU id aa04157; 24 Jan 96 5:12:26 EST Received: (from georg@localhost) by jedlesee.ai.univie.ac.at (8.6.10/8.6.9) id LAA23480 for connectionists@cs.cmu.edu; Wed, 24 Jan 1996 11:09:06 +0100 From: Georg Dorffner Message-Id: <199601241009.LAA23480@jedlesee.ai.univie.ac.at> Subject: C.f.Abstracts: NN in Biomedical Systems To: connectionists@cs.cmu.edu Date: Wed, 24 Jan 1996 11:09:06 +0100 (MET) X-Mailer: ELM [version 2.4 PL24] Mime-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Length: 5134 Following the general call for papers for EANN '96 (Int. Coference on Engineering Applications of Neural Networks), we are still soliciting abstracts for the ========================================= Special Track on Neural Networks in Biomedical Systems ========================================= Any application of neural networks in the medical domain will be welcome. Examples are: - biosignal processing (e.g. EEG, ECG, intensive care, etc.) - biomedical image processing (e.g. in radiology, dermatology, etc.) - diagnostic support in medicine - topographical mapping of diseases or syndromes - epidemological studies - control of biomedical devices (e.g. heart/lung machines, respirators, etc.) - optimization of therapy - monitoring (e.g. in intensive care) - and many more Special emphasis will be put on careful validation of results to make clear the value of neural networks in the application (e.g. through cross-validation with mutiple training sets and comparison to alternatives, such as linear methods). One-page abstracts can be submitted until =============== Feb. 15, 1996 =============== Please state clearly what data was used (number of input features, number of training and test samples) and your results (e.g. by reporting mean performance and standard deviation from a cross-validation). Final papers will be due around March 21, 1996. It is planned to publish the best-quality papers in a special issue of a journal. Abstracts should be emailed to: ====================== georg@ai.univie.ac.at ====================== Below is a description of the EANN conference. Georg Dorffner Dept. of Medical Cybernetics and Artificial Intelligence University of Vienna Freyung 6/2 A-1010 Vienna, Austria phone: +43-1-53532810 fax: +43-1-5320652 email: georg@ai.univie.ac.at http://www.ai.univie.ac.at/oefai/nn/georg.html ---------- International Conference on Engineering Applications of Neural Networks (EANN '96) London, UK 17--19 June 1996 The conference is a forum for presenting the latest results on neural network applications in technical fields. The applications may be in any engineering or technical field, including but not limited to systems engineering, mechanical engineering, robotics, process engineering, metallurgy, pulp and paper technology, aeronautical engineering, computer science, machine vision, chemistry, chemical engineering, physics, electrical engineering, electronics, civil engineering, geophysical sciences, biotechnology, environmental engineering, and biomedical engineering. Abstracts of one page (200 to 400 words) should be sent by e-mail in PostScript format or ASCII. Please mention two to four keywords, and whether you prefer it to be a short paper or a full paper. The short papers will be 4 pages in length, and full papers may be upto 8 pages. Submissions will be reviewed and the number of full papers will be very limited. For more information on EANN'96, please see http://www.lpac.ac.uk/EANN96 and for reports on EANN '95, contents of the proceedings, etc. please see http://www.abo.fi/~abulsari/EANN95.html Five special tracks are being organised in EANN '96: Computer Vision (J. Heikkonen, Jukka.Heikkonen@jrc.it), Control Systems (E. Tulunay, ersin-tulunay@metu.edu.tr), Mechanical Engineering (A. Scherer, andreas.scherer@fernuni-hagen.de), Robotics (N. Sharkey, N.Sharkey@dcs.shef.ac.uk), and Biomedical Systems (G. Dorffner, georg@ai.univie.ac.at) Organising committee A. Bulsari (Finland) D. Tsaptsinos (UK) T. Clarkson (UK) International program committee G. Dorffner (Austria) S. Gong (UK) J. Heikkonen (Italy) B. Jervis (UK) E. Oja (Finland) H. Liljenstr\"om (Sweden) G. Papadourakis (Greece) D. T. Pham (UK) P. Refenes (UK) N. Sharkey (UK) N. Steele (UK) D. Williams (UK) W. Duch (Poland) R. Baratti (Italy) G. Baier (Germany) E. Tulunay (Turkey) S. Kartalopoulos (USA) C. Schizas (Cyprus) J. Galvan (Spain) M. Ishikawa (Japan) D. Pearson (France) Registration information for the International Conference on Engineering Applications of Neural Networks (EANN '96) The conference fee will be sterling pounds (GBP) 300 until 28 February, and sterling pounds (GBP) 360 after that. At least one author of each accepted paper should register by 21 March to ensure that the paper will be included in the proceedings. The conference fee can be paid by a bank draft (no personal cheques) payable to EANN '96, to be sent to EANN '96, c/o Dr. D. Tsaptsinos, Kingston University, Mathematics, Kingston upon Thames, Surrey KT1 2EE, UK. The fee includes attendance to the conference and the proceedings. Registration form can be picked up from the www (or can be sent to you by e-mail) and can be returned by e-mail (or post or fax) once the conference fee has been sent. A registration form sent before the payment of the conference fee is not valid. For more information, please ask eann96@lpac.ac.uk From David_Redish@GS151.SP.CS.CMU.EDU Fri Jan 26 10:03:50 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 26 Jan 96 10:03:43 -0600; AA28443 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 26 Jan 96 10:03:38 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa23637; 24 Jan 96 15:39:02 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa23633; 24 Jan 96 15:25:58 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa00960; 24 Jan 96 15:25:32 EST Received: from GS151.SP.CS.CMU.EDU by B.GP.CS.CMU.EDU id aa12336; 24 Jan 96 12:01:18 EST Received: from localhost by GS151.SP.CS.CMU.EDU id aa13598; 24 Jan 96 12:00 EST To: connectionists@cs.cmu.edu Cc: dredish@cs.cmu.edu Subject: new web site for NIPS*95 papers Reply-To: dredish@cs.cmu.edu Date: Wed, 24 Jan 1996 12:00:37 -0500 Message-Id: <13596.822502837@GS151.SP.CS.CMU.EDU> From: David Redish Many of the papers presented at NIPS*95 have been made available online by their authors. The NIPS Foundation now maintains a web site where abstracts and URLs for these papers are collected: http://www.cs.cmu.edu/Web/Groups/NIPS/NIPS95/Papers.html New papers are being added regularly. The complete list of papers presented at NIPS*95 is available on the NIPS home page. The printed NIPS*95 proceedings will be available from MIT Press in May. ------------------------------------------------------------ David Redish Computer Science Department CMU graduate student Neural Processes in Cognition Training Program Center for the Neural Basis of Cognition http://www.cs.cmu.edu/Web/People/dredish/home.html ------------------------------------------------------------ maintainer, CNBC website: http://www.cs.cmu.edu/Web/Groups/CNBC maintainer, NIPS*96 website: http://www.cs.cmu.edu/Web/Groups/NIPS ------------------------------------------------------------ From kyana@bme.ei.hosei.ac.jp Fri Jan 26 10:03:51 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 26 Jan 96 10:03:37 -0600; AA28439 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 26 Jan 96 10:03:30 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa23613; 24 Jan 96 15:26:18 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa23611; 24 Jan 96 15:03:52 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa00925; 24 Jan 96 15:03:33 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa08229; 24 Jan 96 4:22:33 EST Received: from yana-gate.bme.ei.hosei.ac.jp by RI.CMU.EDU id aa03993; 24 Jan 96 4:18:42 EST Received: from [133.25.93.12] (yana0c.bme.ei.hosei.ac.jp [133.25.93.12]) by yana01.bme.ei.hosei.ac.jp (8.6.12+2.4W/3.3W-bme-3.0) with SMTP id SAA18449; Wed, 24 Jan 1996 18:14:06 +0900 Date: Wed, 24 Jan 1996 18:14:06 +0900 Message-Id: <199601240914.SAA18449@yana01.bme.ei.hosei.ac.jp> To: Connectionists@cs.cmu.edu From: Kazuo Yana X-Sender: kyana@yana01.bme.ei.hosei.ac.jp Subject: invitation to BSI96 Cc: kyana@bme.ei.hosei.ac.jp Mime-Version: 1.0 Content-Type: text/plain; charset=iso-2022-jp X-Mailer: Eudora-J(1.3.8.5-J13) THE 2ND IFMBE-IMIA INTERNATIONAL WORKSHOP ON BIOSIGNAL INTERPRETATION (BSI96) September 23 - 28, 1996 Kanagawa, JAPAN CALL FOR PAPER SCOPE OF THE WORKSHOP The International Federation for Medical and Biological Engineering (IFMBE) and the International Medical Informatics Association (IMIA), in collaboration with the Japan Society of Medical Electronics and Biological Engineering, will organize the Second Workshop on Biosignal Interpretation (BSI96). This workshop aims to explore in the relatively new field of biosignal interpretation: model based biosignal analysis, interpretation and integration, extending existing signal processing technology for the effective utilization of biosignals in a practical environment and in a deeper understanding of biological functions. This is the second workshop in this area. The first workshop, IMIA-IFMBE Working Conference on Biosignal Interpretation, was held at Skorping, Denmark in August, 1993. SCIENTIFIC PROGRAM Prospective authors are invited to propose original contributions which meet the general scope mentioned above in any of the following subject categories. (1) Mathematical modeling of experimental and clinical biosignlas (nonlinear phenomena, chaos, fractals, neural network modeling, cardiovascular and respiratory fluctuations analysis, ECG/ EEG/ EMG signal modeling, potential mapping, inverse problem, miscellaneous) (2) Biosignal processing and pattern analysis (nonstationary/nonlinear analysis, time frequency analysis, statistical time series analysis, signal detection, signal reconstruction, neural network, wavelet analysis, recording and display instrumentation, miscellaneous) (3) On-line interactive signal acquisition and processing (intelligent monitoring, ambulatory system, miscellaneous) (4) Decision-support methods (parameter estimation, decision making, rule based/expert systems, automatic diagnoses, data reasoning, man-machine interface, miscellaneous). For in depth discussion, enough time will be assigned for all oral and poster presentations. Besides paper presentations, real system/software demonstrations are encouraged. PUBLICATION All papers will be published in the workshop proceedings. 30-40 papers will be selected to be published as a regular paper in the IMIA official journal: Methods of Information in Medicine. IMPORTANT DEADLINES 1. Submission of abstract (500 words or less): February 29, 1996. 2. Notification of Acceptance: April 15, 1996. 3. Submission of full length paper: July 15, 1996. ABSTRACT FORMAT (due by February 29, 1996) Title (Centered) Author(s) and Affiliation(s) (Centered) Abstract (500 words or less) should be sent to the conference secretariat (Professor Kazuo Yana,Department of Electronic Informatics, Hosei University, Koganei City Tokyo 184 JAPAN) by February 29, 1996. The abstract should be single spaced and clearly typed on A4 or letter size paper and have appropriate margins (Approx 2cm or 1 inch.) . After you receive notification of acceptance of your paper (by April 15), prepare for a camera ready conference paper (max 4 printed pages). The format will be sent to all the　perticipant s with the notification of the acceptance by April 15. The paper is due by July 15. Selected papers will be publised as a regular paper as is or with minor revision in Methods of Information in Medicine. CONFERENCE SITE Shonan Village Center, Hayama-machi, Kanagawa 240-01, Japan Phone: +81-468-55-1800 FAX:+81-468-55-1816. The Shonan Village Center which offers the workshop facilities and accomodations is situated on a Shonan hill in the central part of the Miura Peninshula, commanding a view of Mt. Fuji and over looking Sagami Bay. Sagami Bay is famous place for marine sports. There are other sports facilities nearby such as golf courses and tennis courts. The area is known as the holiday resort closest to Tokyo. Proximity to the anciet capital city of Kamakura, exotic harbor city of Yokohama and other tourist spots may add an attractive feature to the site for participants in planning after-conference tours. PARTICIPATION FEES (Tentative) Registration (includes the workshop proc., coferenced materials, banquet ticket): Before July 31... 25,000 YEN (15,000 YEN for students) After July 31... 30,000 YEN (20,000 YEN for students) Accomodation (5 nights including meals and services ): 75,000 YEN/Person (65,000 YEN/Person for students) The registration form will be sent by April 15 with notification of acceptance and a tentative program. A limited number of accomodations for observers and accompanying persons are available. ORGANIZATION General Chair Kajiya, Fumihiko ( Kawasaki Medical School: kajiya@me.kawasaki-m.ac.jp) Executive Committee Co-Chairs Sato, Shunsuke (Osaka University: sato@bpe.es.osaka-u.ac.jp) Takahashi, Takashi( Kyoto University: tak@kuhp.kyoto-u.ac.jp) Scientific Program Co-Chairs van Bemmel, Jan H. (Rotterdam, NL); Saranummi, Niilo (Tampere, FIN) International Scientific Program Committee: Cerutti, Sergio (Milano, I); Dawant, Benoit(Nashville, USA) Jansen, Ben H. (Houston, USA); Kaplan, Danny (Montreal, CA) Kitney, Richard I. (London, UK); Rosenfalck, Annelise (Aalborg, DK) Rubel, Paul (Lyon, F); Sato, Shunsuke (Osaka, J) Saul, Philip (Cambridge, USA); Zywietz, Christoph (Hanover, FRG) Executive Committee Bin, He (University of Illinois at Chicago) Hayano, Jun-ichiro (Nagoya City University) Ichimaru, Yuhei (Dokkyo University) Kiryu, Tohru (Niigata University) Musha, Toshimitsu(Keio University/Brain Functions Laboratory, Inc.) Okuyama, Fumio (Tokyo Medical and Dental University) Yamamoto, Mitsuaki (Tohoku University) Yamamoto, Yoshiharu (Tokyo University) Yana, Kazuo (Hosei University) For further information, please contact: Professor Kazuo Yana Secretariat, the 2nd IFMBE-IMIA Workshop on Biosignal Interpretation Dept. Electronic Informatics, Hosei University, Koganei Tokyo 184 JAPAN Phone/FAX: +81-(0)423-87-6188 E-mail: kyana@bme.ei.hosei.ac.jp Internet Home Page: http://www.bme.ei.hosei.ac.jp/BSI96/ From giles@research.nj.nec.com Fri Jan 26 10:03:52 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 26 Jan 96 10:03:47 -0600; AA28453 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 26 Jan 96 10:03:44 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa25234; 25 Jan 96 17:59:55 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa25232; 25 Jan 96 17:36:26 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa02042; 25 Jan 96 17:35:59 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa04269; 25 Jan 96 17:06:40 EST Received: from zingo.nj.nec.com by CS.CMU.EDU id aa02774; 25 Jan 96 17:05:59 EST Received: by zingo (940816.SGI.8.6.9/YDL1.4-910307.16) id RAA02162(zingo); Thu, 25 Jan 1996 17:02:40 -0500 Received: by alta (5.52/cliff's joyful mailer #2) id AA06686(alta); Thu, 25 Jan 96 17:02:39 EST Date: Thu, 25 Jan 96 17:02:39 EST From: Lee Giles Message-Id: <9601252202.AA06686@alta> To: connectionists@cs.cmu.edu Subject: TR available: PRODUCT UNIT LEARNING The following Technical Report is available via the University of Maryland Department of Computer Science and the NEC Research Institute archives: (A short version of this TR was published in NIPS7) _____________________________________________________________________ PRODUCT UNIT LEARNING Technical Report UMIACS-TR-95-80 and CS-TR-3503, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742 Laurens R. Leerink{a}, C. Lee Giles{b,c}, Bill G. Horne{b}, Marwan A.Jabri{a} {a}SEDAL, Dept. of Electrical Engineering, The U. of Sydney, Sydney, NSW 2006, Australia {b}NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA {c}UMIACS, U. of Maryland, College Park, MD 20742, USA ABSTRACT Product units provide a method of automatically learning the higher-order input combinations required for the efficient synthesis of Boolean logic functions by neural networks. Product units also have a higher information capacity than sigmoidal networks. However, this activation function has not received much attention in the literature. A possible reason for this is that one encounters some problems when using standard backpropagation to train networks containing these units. This report examines these problems, and evaluates the performance of three training algorithms on networks of this type. Empirical results indicate that the error surface of networks containing product units have more local minima than corresponding networks with summation units. For this reason, a combination of local and global training algorithms were found to provide the most reliable convergence. We then investigate how `hints' can be added to the training algorithm. By extracting a common frequency from the input weights, and training this frequency separately, we show that convergence can be accelerated. A constructive algorithm is then introduced which adds product units to a network as required by the problem. Simulations show that for the same problems this method creates a network with significantly less neurons than those constructed by the tiling and upstart algorithms. In order to compare their performance with other transfer functions, product units were implemented as candidate units in the Cascade Correlation (CC) {Fahlman90} system. Using these candidate units resulted in smaller networks which trained faster than when the any of the standard (three sigmoidal types and one Gaussian) transfer functions were used. This superiority was confirmed when a pool of candidate units of four different nonlinear activation functions were used, which have to compete for addition to the network. Extensive simulations showed that for the problem of implementing random Boolean logic functions, product units are always chosen above any of the other transfer functions. -------------------------------------------------------------------------- -------------------------------------------------------------------------- http://www.neci.nj.nec.com/homepages/giles.html http://www.cs.umd.edu/TRs/TR-no-abs.html or ftp://ftp.nj.nec.com/pub/giles/papers/UMD-CS-TR-3503.product.units.neural.nets.ps.Z ---------------------------------------------------------------------------- -- C. Lee Giles / Computer Sciences / NEC Research Institute / 4 Independence Way / Princeton, NJ 08540, USA / 609-951-2642 / Fax 2482 www.neci.nj.nec.com/homepages/giles.html ==