From payman@ebs330.eb.uah.edu Sun Oct 15 18:18:04 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Sun, 15 Oct 95 18:18:01 -0500; AA06821 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sun, 15 Oct 95 18:17:58 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab04021; 15 Oct 95 16:59:19 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab04019; 15 Oct 95 16:39:09 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa02434; 15 Oct 95 16:38:55 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa08351; 13 Oct 95 18:47:49 EDT Received: from uahis1.uah.edu by CS.CMU.EDU id aa25299; 13 Oct 95 18:46:32 EDT Received: from ebs330 by uahis1.uah.edu with SMTP ; Fri, 13 Oct 95 17:46:01 CST Received: from anahita.eb.uah.edu.uah.edu by ebs330 (4.1/SMI-4.1) id AA24566; Fri, 13 Oct 95 17:47:14 CDT Date: Fri, 13 Oct 95 17:47:14 CDT From: Payman Arabshahi Message-Id: <9510132247.AA24566@ebs330> To: connectionists@cs.cmu.edu Subject: Computational Intelligence in Financial Engineering - CIFEr'96 Call for Papers Conference on Computational Intelligence for Financial Engineering CIFEr Conference =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- Visit us on the World Wide Web for latest updates and information at http://www.ieee.org/nnc/conferences/cfp/cifer96.html The homepage can also be accessed via the "Conferences" page of the IEEE Neural Network Council's Homepage at http://www.ieee.org/nnc The deadlines mentioned in this CFP supercede those in the hard copy version. Our homepage will be updated on October 15 to reflect these new deadlines. -- 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/ ---------------------------------------------------------------------- IEEE/IAFE 1996 $$$$$$$$$$$ $$$$$$ $$$$$$$$$$$ $$$$$$$$$$ $$$$$$$$$$$ $$$$$$ $$$$$$$$$$$ $$$$$$$$$$ $$$$ $$ $$$$ $$$$ $$$ $$$ $$$$ $$$$ $$$$$$$ $$$$$$ $$$$$$$$$$ $$$$ $$$$ $$$$$$$ $$$$$$ $$$$$$$$$$ $$$$ $$ $$$$ $$$$ $$$ $$$ $$$ $$$$$$$$$$$ $$$$$$ $$$$ $$$$$$$$$$ $$$ $$$$$$$$$$$ $$$$$$ $$$$ $$$$$$$$$$ $$$ Call for Papers Conference on Computational Intelligence for Financial Engineering CIFEr Conference March 24-26, 1996, New York City, Crowne Plaza Manhattan Sponsors: The IEEE Neural Networks Council, The International Association of Financial Engineers The IEEE/IAFE CIFEr Conference is the second annual collaboration between the professional engineering and financial communities, and is one of the leading forums for new technologies and applications in the intersection of computational intelligence and financial engineering. Intelligent computational systems have become indispensable in virtually all financial applications, from portfolio selection to proprietary trading to risk management. Topics in which papers, panel sessions, and tutorial proposals are invited include, but are not limited to, the following: CONFERENCE TOPICS ----------------- > Financial Engineering Applications: Trading Systems Forecasting Hedging Strategies Risk Management Pricing of Structured Securities Systemic Risk Asset Allocation Exotic Options > Computer & Engineering Applications & Models: Neural Networks Probabilistic Reasoning Fuzzy Systems and Rough Sets Stochastic Processes Dynamic Optimization Time Series Analysis Non-linear Dynamics Evolutionary Computation INSTRUCTIONS FOR AUTHORS, PANEL PROPOSALS, SPECIAL SESSIONS, TUTORIALS ---------------------------------------------------------------------- All summaries and proposals for tutorials, panels and special sessions must be received by the conference Secretariat at Meeting Management by December 1, 1995. Our intentions are to publish a book with the best selection of papers accepted. AUTHORS (FOR CONFERENCE ORAL SESSIONS) -------------------------------------- One copy of the Extended Summary (not exceeding four pages of 8.5 inch by 11 inch size) must be received by Meeting Management by December 1, 1995. Centered at the top of the first page should be the paper's complete title, author name(s), affiliation(s), and mailing addresses(es). Fonts no smaller than 10 pt should be used. Papers must report original work that has not been published previously, and is not under consideration for publication elsewhere. In the letter accompanying the submission, the following information should be included: * Topic(s) * Full title of paper * Corresponding Author's name * Mailing address * Telephone and fax * E-mail (if available) * Presenter (If different from corresponding author, please provide name, mailing address, etc.) Authors will be notified of acceptance of the Extended Summary by January 10, 1996. Complete papers (up to a maximum of seven 8.5 inch by 11 inch pages) will be due by February 9, 1996, and will be published in the conference proceedings. SPECIAL SESSIONS ---------------- A limited number of special sessions will address subjects within the topical scope of the conference. Each special session will consist of from four to six papers on a specific topic. Proposals for special sessions will be submitted by the session organizer and should include: * Topic(s) * Title of Special Session * Name, address, phone, fax, and email of the Session Organizer * List of paper titles with authors' names and addresses * One page of summaries of all papers Notification of acceptance of special session proposals will be on January 10, 1995. If a proposal for a special session is accepted, the authors will be required to submit a camera ready copy of their paper for the conference proceedings by February 9, 1996. PANEL PROPOSALS --------------- Proposals for panels addressing topics within the technical scope of the conference will be considered. Panel organizers should describe, in two pages or less, the objective of the panel and the topic(s) to be addressed. Panel sessions should be interactive with panel members and the audience and should not be a sequence of paper presentations by the panel members. The participants in the panel should be identified. No papers will be published from panel activities. Notification of acceptance of panel session proposals will be on January 10, 1996. TUTORIAL PROPOSALS ------------------ Proposals for tutorials addressing subjects within the topical scope of the conference will be considered. Proposals for tutorials should describe, in two pages or less, the objective of the panel and the topic(s) to be addressed. A detailed syllabus of the course contents should also be included. Most tutorials will be four hours, although proposals for longer tutorials will also be considered. Notification of acceptance of tutorial proposals will be on January 10, 1996. EXHIBIT INFORMATION ------------------- Businesses with activities related to financial engineering, including software & hardware vendors, publishers and academic institutions, are invited to participate in CIFEr's exhibits. Further information about the exhibits can be obtained from the CIFEr-secretariat, Barbara Klemm. SPONSORS -------- Sponsorship for the CIFEr Conference is being provided by the IAFE (International Association of Financial Engineers) and the IEEE Neural Networks Council. The IEEE (Institute of Electrical and Electronics Engineers) is the world's largest engineering and computer science professional non-profit association and sponsors hundreds of technical conferences and publications annually. The IAFE is a professional non-profit financial association with members worldwide specializing in new financial product design, derivative structures, risk management strategies, arbitrage techniques, and application of computational techniques to finance. Early registration is $400 for IEEE (Institute of Electrical and Electronic Engineers, Neural Networks Council) and IAFE (International Association of Financial Engineers) members. For details contact Barbara Klemm at Meeting Management. INFORMATION ----------- CIFEr Secretariat: Meeting Management IEEE/IAFE Computational Intelligence for Financial Engineering 2603 Main Street, Suite #690 Irvine, California 92714 Tel: (714) 752-8205 or (800) 321-6338 Fax: (714) 752-7444 Email: 74710.2266@compuserve.com Visit us on the World Wide Web for latest updates: http://www.ieee.org/nnc/conferences/cfp/cifer96.html ORGANIZING COMMITTEE -------------------- Keynote Speaker: Stephen Figlewski, Professor of Finance and Editor of the Journal of Derivatives Stern School of Business, New York University John M. Mulvey, Professor and Director Engineering Management Systems Princeton University, Princeton Conference Committee General Co-chairs: John Marshall, Professor of Financial Engineering Polytechnic University, New York, NY Robert Marks, Professor of Electrical Engineering, University of Washington, Seattle, WA Program Committee Co-chairs: Benjamin Melamed, Ph.D., Research Scientist RUTCOR-Rutgers University's Center for Operations Research Alan Tucker, Associate Professor of Finance Pace University, New York, NY International Liaison: Arnold Jang, Vice President, Intelligent Trading Systems Springfields Investments Advisory Company, Taipei, Taiwan Organizational Chair: Robert Golan, President Rough Knowledge Discovery Inc., Calgary, Alberta Finance Chair: Ingrid Marshall, Accountant Marshall & Marshall, Stroudsburg, PA Exhibits Chair: Steve Piche, Lead Scientist Pavillion Inc, Austin Program Co-Chair: Alan Tucker and Benjamin Melamed Program Committee: Phelim Boyle, Professor of Accounting University of Waterloo, Waterloo, Ontario Mark Broadie, Associate Professor of Finance Graduate School of Business Columbia University, New York, NY Jan Dash, Ph.D, Managing Director Smith Barney, New York, NY Stephen Figlewski, Professor of Finance New York University, New York, NY Roy S. Freedman, Ph.D, President Inductive Solutions, Inc, New York, NY Peter L. Hammer, Professor and Director RUTCOR-Rutgers University's Center for Operations Research, New Brunswick, NJ Jimmy E. Hilliard, Professor of Finance University of Georgia, Athens, GA John Hull, Professor of Management University of Toronto, Toronto, Ontario Yuval Lirov, Ph.D., Vice President Lehman Brothers, Inc, New York, NY David G. Luenberger, Professor of Electrical Engineering Stanford University, Stanford, CA John M. Mulvey, Professor and Director Engineering Management Systems Princeton University, Princeton, NJ Jason Z. Wei, Associate Professor of Finance University of Saskatchewan, Saskatoon Robert E. Whaley, Professor of Business Futures and Options Research Center Duke University, Durham, NC Publicity Chair Michael Wolf, General Manager Financial Products, The Mathworks, Inc., Natick, MA Electronic Publicity Chair Payman Arabshahi, Assistant Professor of Electrical Engineering University of Alabama in Huntsville, Huntsville Conference Liaison Scott Mathews, Senior Associate Marshall, Tucker, and Associates, Edmonds, WA From pazzani@super-pan.ICS.UCI.EDU Sun Oct 15 20:21:58 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Sun, 15 Oct 95 20:21:51 -0500; AA07756 Received: from paris.ics.uci.edu by lucy.cs.wisc.edu; Sun, 15 Oct 95 20:21:30 -0500 Received: from super-pan.ics.uci.edu by paris.ics.uci.edu id aa05099; 15 Oct 95 14:49 PDT To: ML-LIST:; Subject: Machine Learning List: Vol. 7, No. 17 Reply-To: ml@ics.uci.edu Date: Sun, 15 Oct 1995 14:36:32 -0700 From: Michael Pazzani Message-Id: <9510151449.aa05099@paris.ics.uci.edu> Machine Learning List: Vol. 7, No. 17 Sunday, October 15, 1995 Contents: Hybrid Models: Learning and Architectures mailing lists Special Issue on Data Mining (IEEE TRansactions) ML journal ILP special issue: final deadline extension POSTDOC JOB OPENINGS ICNN-96 June 2-6, 1996 Washington, DC KDD-96 Call for Papers AI 96 - Deadline Approaches 2nd CFP: AAAI'96 Symposium on Multiagent Learning Acquisition, Learning, and Demonstration: Automating Tasks for Users ML-related JAIR articles ICML-96: Final Call for Papers 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 ---------------------------------------------------------------------- From: Ron Sun Date: Thu, 5 Oct 1995 09:13:20 -0500 Subject: Hybrid Models: Learning and Architectures mailing lists ANNOUNCING A NEW MAILING LIST: The Hybrid Models: Learning and Architectures mailing lists. As we discussed at the CSI workshop at IJCAI in this August, we now establish this new mailing list for the specific purpose of exchanging information and ideas regarding hybrid models, especially models integrating symbolic and connectionist processes. Other hybrid models, such as fuzzy logic+neural networks and GA+NN, are also covered. This is an unmoderated list. Conference and workshop announcements, papers and technical reports, informed discussions of specific topics in hybrid model areas, and other pertinent messages are appropriate items for submission. Email your submission to hybrid-list@cs.ua.edu, which will be automatically forwarded to all the recipients of the list. Information regarding subscription is attached below. For questions and suggestions regarding this list, send email to rsun@cs.ua.edu (only if you have to). This mailing list has incorporated the old HYBRID list at Brown U. maintained by Michael Perrone (thanks to Michael), and included names of those who attended the 1995 CSI workshop or expressed interest in it. (To remove your name from the list, see the instruction at the end of this message.) Regards, --Ron Sun ============================================================================== The University of Alabama Department of Computer Science has set up a list service for this: To subscribe to this list service, send an e-mail message to the userid "listproc@cs.ua.edu" with NO SUBJECT, but a one-line text message, as shown below: SUBSCRIBE hybrid-list YourFirstName YourLastName You should receive a response back indicating your addition to the list. After this, you can submit items to the list by simply e-mail'ing a message to the userid: "hybrid-list@cs.ua.edu". The message will automatically be sent to all individuals on the list. To unsubscribe to this list service, send an e-mail message to the userid "listproc@cs.ua.edu" with NO SUBJECT, but a one-line text message, as shown below: UNSUBSCRIBE hybrid-list ============================================================================== ------------------------------ From: han@cs.sfu.ca Subject: Special Issue on Data Mining (IEEE TRansactions) Date: Fri, 13 Oct 95 19:35:51 PDT CALL FOR PAPERS Special Issue on Database Mining IEEE Trans. on Knowledge and Data Engineering Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields, including database systems, knowledge-base systems, artificial intelligence, machine learning, knowledge acquisition, statistics, spatial databases, and data visualization, have shown great interest in database mining. Furthermore, several emerging applications in information providing services, such as on-line services and World Wide Web, also call for various data mining techniques to better understand user behavior, to meliorate the service provided, and to increase the business opportunities. A special issue of IEEE Transactions on Knowledge and Data Engineering will be devoted to this topic. Areas of interest include, but not limited to, the following: Principles of Data Mining and Knowledge Discovery Tools, Techniques, and Performance Improvements for Database Mining Data Representation, Knowledge Visualization and Interactive Database Mining Parallel and Distributed Algorithms for Database Mining Integration of Different Database Mining Capabilities and Methods Knowledge Discovery in Spatial, Temporal, and Heterogeneous Databases Knowledge Discovery Systems and Implementations Database Mining Applications Instructions for Submitting Papers Manuscript should be no more than 25 typewritten pages with a 12-point font and double spacing, including figures and references. Papers must not have been published previously or currently submitted for publication elsewhere. Each manuscript should have a title page with the title of the paper, full name(s) and affiliation(s) of author(s), complete postal and electronic addresses, telephone number(s), a FAX number, an informative 150-200 words abstract, and a list of identifying keywords. Send six copies of each submission to one of the guest editors. For further information, contact the guest editors. Important Dates Manuscript due: Feb. 1, 1996 Acceptance Notification: May 15, 1996 Final manuscript due: July 1, 1996 Publication date of issue: Dec. 1996 Guest Editors: Philip S. Yu, Ming-Syan Chen, and Jiawei Han Dr. Philip S. Yu IBM Thomas J. Watson Research Ctr. P.O. Box 704 Yorktown Heights, NY 10598 Tel: (914) 784-7141 email: psyu@watson.ibm.com Dr. Ming-Syan Chen IBM Thomas J. Watson Research Ctr. P.O. Box 704 Yorktown Heights, NY 10598 Tel: (914) 784-7517 email: mschen@watson.ibm.com Prof. Jiawei Han School of Computing Science Simon Fraser University B.C., Canada V5A 1S6 Tel: (604) 291-4411 email: han@cs.sfu.ca ------------------------------ From: David.Page@comlab.ox.ac.uk Subject: ML journal ILP special issue: final deadline extension Date: Mon, 2 Oct 95 10:57:23 BST ********************************************************** IMPORTANT UPDATE---DEADLINE EXTENDED TO NOVEMBER 15 FOR: ********************************************************** Special Issue: INDUCTIVE LOGIC PROGRAMMING MACHINE LEARNING The deadline for the special issue of the journal Machine Learn- ing, on Inductive Logic Programming, has been extended a final time, to NOVEMBER 15, 1995. (This extension is due to a con- flicting deadline for many researchers in the area.) We repeat the Call for Papers for this special issue, below. ********************************************************** CALL FOR PAPERS ********************************************************** Special Issue: INDUCTIVE LOGIC PROGRAMMING MACHINE LEARNING Edited by Stephen Muggleton and David Page Oxford University Computing Laboratory Inductive Logic Programming (ILP) is a growing research area spawned by Machine Learning and Logic Programming. While the in- fluence of Logic Programming has encouraged the development of strong theoretical foundations, the new area has inherited its experimental orientation from Machine Learning. Already ILP has been applied successfully to a variety of complex problems, in- cluding structure-activity and mutagenicity prediction of pharma- ceutical chemicals, protein secondary-structure prediction, finite-element mesh design, and optimal play in chess endgames. For this special issue we encourage submission of papers describ- ing novel ILP algorithms, experimental applications, or theoreti- cal results. Submission deadline: NOVEMBER 15, 1995 It is the editors' intention to publish the special issue as a book as well. Papers should be double spaced and 8,000 to 12,000 words in length, with full-page figures counting for 400 words. All sub- missions will be subject to the standard review procedure. Send three (3) copies of submissions to: David Page Phone: +44-865-283-520 Oxford University Computing Lab Fax: +44-865-273-839 Wolfson Building David.Page@prg.ox.ac.uk Parks Road Oxford, OX1 3QD U. K. Also mail five (5) copies of submitted papers to: Karen Cullen Phone: (617) 871-6300 MACHINE LEARNING Editorial Office karen@world.std.com Kluwer Academic Publishers 101 Philip Drive Norwell, MA 02061 U. S. A. Note: Machine Learning is now accepting submission of final copy in electronic form. A latex style file and related files are available via anonymous ftp from ftp.std.com. Look in Kluwer/styles/journals for the files README, smjrnl.doc, smjrnl.sty, smjsamp.tex, smjtmpl.tex, or smjstyles.tar (which contains them all). ------------------------------ From: Luca Gambardella Subject: POSTDOC JOB OPENINGS Date: Wed, 27 Sep 95 17:13:12 +0100 IDSIA: POSTDOC JOB OPENINGS Statistics, Neural Nets, Forecasting, Planning, Optimization IDSIA - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale is a machine learning research center located in Lugano (Switzerland). IDSIA receives subsidies from both private and public sectors. NEW PROJECT. Starting 1996, IDSIA will collaborate with a private company producing software tools for supporting container terminal organization and resource allocation. Goals of the project are: (1) Forecasting the container terminal's input/output flow, using statistical models, neural nets, etc., and taking expert knowledge into account. There is a huge data base describing the terminal activity in previous years. IDSIA has considerable expertise in this area. (2) Finding optimal container positions in the stockage area. Where to place an incoming container? This depends on many parameters: final container destination, size and content, the next carrier, current occupancy of the container parking area (often, to grab one container others need to be rearranged). Emphasis is on optimization and planning. IDSIA's role is to define methodologies for solving problems (1) and (2). The private company's role is to produce a collection of software tools integrated in the existing industrial software environment. The 2 year project is supported by Swiss Government funds (CERS/KWF), and will involve 6 man years. In the first (second) year, there will be two (one) IDSIA researcher(s) and one (two) company employee(s). IDSIA has two immediate openings (one for 1 year, other for 2 years). Required expertise: Ph.D. in computer science, statistics (or similar) experience in forecasting (statistics, neural nets, etc.). experience in optimization and planning experience in industrial applications Please send postscripts of resume, description of current interests, names of three referees, and other related information to: Luca Gambardella IDSIA C.so Elvezia 36 6900 Lugano CH email: luca@idsia.ch DEADLINE: OCTOBER 31. ------------------------------ From: Amanda Nevin Subject: ICNN-96 June 2-6, 1996 Washington, DC Date: Thu, 21 Sep 95 14:43:52 -0500 (http://www-ece.rice.edu/96icnn/) INTERNATIONAL CONFERENCE ON NEURAL NETWORKS (ICNN'96) Sheraton Washington Hotel Washington, DC, USA TUTORIALS: June 2, 1996 CONFERENCE: June 3-6, 1996 Important Dates and Deadlines October 1, 1995 Proposals to Special Sessions Chair October 16, 1995 Papers received by Program Chair November 1, 1995 Proposals to Panel Sessions Chair December 1, 1995 Exhibit requirements to General Chair January 23, 1996 Authors informed of decisions February 23, 1996 Final papers to Program Chair General Chair Program Chair Benjamin Wah Bing Sheu Coordinated Science Lab. Dept. of Electrical Engr. Univ. of Illinois, Univ. of Southern California at Urbana-Champaign EP/Powell Hall 604 Urbana, IL 61801 Los Angeles, CA 90089-0271 icnn96@manip.crhc.uiuc.edu icnn96@pacific.usc.edu phone: (217) 333-3516 phone: (213) 740-4711 fax: (217) 244-7175 fax: (213) 740-8677 Panel Session Chair Special Session Chair Mohammed Ismail Jacek M. Zurada Dept. of Electrical Engr. Dept. of Electrical Engr. Ohio State University Univ. of Louisville 2015 Neil Avenue Louisville, KY 40292, USA Columbus, Ohio 43210-1272 jmzura02@starbase. ismail@ee.eng.ohio-state.edu spd.louisville.edu phone: (614) 292-0351 phone: (502) 852-6314 fax: (614) 292-7596 fax: (502) 852-6807 HOMEPAGE. Conference information is maintained by the Publicity Chair, Joseph R. Cavallaro, on the World Wide Web at http://www-ece.rice.edu/96icnn -- Mandy Nevin (713) 527-4025 Dept. of ECE, MS-366 Rice University Houston, TX 77251-1892 mandynev@ece.rice.edu ------------------------------ From: KDD-96 Account Subject: KDD-96 Call for Papers Date: Wed, 4 Oct 95 18:42:45 PDT ========================================================================= C a l l F o r P a p e r s ========================================================================= The Second International Conference on Knowledge Discovery and Data Mining (KDD-96) Portland, Oregon, USA, August 3-5, 1996 ======================================= Sponsored by AAAI and Collocated with AAAI-96 and UAI-96. visit the KDD-96 WWW page at http://www-aig.jpl.nasa.gov/kdd96 Knowledge Discovery in Databases (KDD), also referred to as Data Mining, is an area of common interest to researchers in machine discovery, statistics, databases, knowledge acquisition, machine learning, data visualization, high performance computing, and knowledge-based systems. The rapid growth of data and information has created a need and an opportunity for extracting knowledge from databases, and both researchers and application developers have been responding to that need. KDD applications have been developed for astronomy, biology, finance, insurance, marketing, medicine, and many other fields. The first international conference on Knowledge Discovery and Data Mining (KDD-95), held in Montreal in August 1995, was an outstanding success, attracting over 340 participants. The second international conference will follow up the success of KDD-95 by bringing together researchers and application developers from different areas focusing on unifying themes. The topics of interest include, but are not limited to: Theory and Foundational Issues in KDD: Data and knowledge representation for KDD Probabilistic modeling and uncertainty management in KDD Modeling of structured, unstructured and multimedia data Metrics for evaluation of KDD results Fundamental advances in search, retrieval, and discovery methods Definitions, formalisms, and theoretical issues in KDD Data Mining Methods and Algorithms: Algorithmic complexity, efficiency and scalability issues in data mining Probabilistic and statistical models and methods Using prior domain knowledge and re-use of discovered knowledge Parallel and distributed data mining techniques High dimensional datasets and data preprocessing Unsupervised discovery and predictive modeling KDD Process and Human Interaction: Models of the KDD process Methods for evaluating subjective relevance and utility Data and knowledge visualization Interactive data exploration and discovery Privacy and security Applications: Data mining systems and data mining tools Application of KDD in business, science, medicine and engineering Application of KDD methods for mining knowledge in text, image, audio, sensor, numeric, categorical or mixed format data Resource and knowledge discovery using the Internet This list of topics is not intended to be exhaustive but an indication of typical topics of interest. Prospective authors are encouraged to submit papers on any topics of relevance to knowledge discovery and data mining. DEMONSTRATION SESSIONS: KDD-96 also invites working demonstrations of discovery systems. Exact details on how to arrange a demo at KDD-96 will be forthcoming. SUBMISSION AND REVIEW CRITERIA: Both research and applications papers are solicited. All submitted papers will be reviewed on the basis of technical quality, relevance to KDD, novelty, significance, and clarity. Authors are encouraged to make their work accessible to readers from other disciplines by including a carefully written introduction. Papers should clearly state their relevance to KDD. Please submit 5 *hardcopies* of a short paper (a maximum of 9 single-spaced pages not including cover page but including bibliography, 1 inch margins, and 12pt font) to be received by March 18, 1996. A cover page must include author(s) full address, E-MAIL, paper title and a 200 word abstract, and up to 5 keywords. This cover page must accompany the paper. In addition, an ASCII version of the cover page should be sent electronically via email to kdd96@almaden.ibm.com by March 18th 1995 (preferably earlier for e-mail). For the electronic title page, authors are required to use the template made available by ftp. Please visit http://www-aig.jpl.nasa.gov/kdd96/ to retrieve the electronic template. Please mail the 5 hardcopies of the full papers to : AAAI (KDD-96) 445 Burgess Drive Menlo Park, CA 94025-3496 U.S.A. Phone: (+1 415) 328-3123; Fax: (+1 415) 321-4457 Email: kdd@aaai.org *********** I m p o r t a n t D a t e s **************** * 5 copies of full papers received by: March 18, 1996 * * (in addition to an electronic ASCII title page) * * acceptance notices: April 19, 1996 * * final camera-readies due to AAAI by: May 20, 1996 * ********************************************************** KDD-96 Organization: ==================== General Conference Chair: Usama M. Fayyad, Jet Propulsion Laboratory KDD-96 Publicity Chair: Padhraic Smyth, Jet Propulsion Laboratory KDD Sponsorship Chair: Gregory Piatetsky-Shapiro, GTE Laboratories Program Co-chairs: ================== Evangelos Simoudis (IBM Almaden Research Center) Jia Wei Han (Simon Fraser University) Program Committee ================= Rakesh Agrawal (IBM Almaden Research Center, USA) Tej Anand (AT&T Global Information Solutions, USA) Ron Brachman (AT&T Bell Laboratories, USA) Wray Buntine (Heuristicrats Research, USA) Nick Cercone (University of Regina, Canada) Peter Cheeseman (NASA AMES Research Center, USA) Bruce Croft (University of Massachusetts at Amherst, USA) Steve Eick (AT&T Bell Laboratories, USA) Usama Fayyad (Jet Propulsion Laboratory, USA) Clark Glymour (Carnegie-Mellon University, USA) George Grinstein (University of Lowell, USA) David Hand (Open University, UK) David Heckerman (Microsoft Corporation, USA) Se June Hong (IBM T.J. Watson Research Center, USA) Tomasz Imielinski (Rutgers University, USA) Larry Jackel (AT&T Bell Laboratories, USA) Larry Kerschberg (George Mason University, USA) Willi Kloesgen (GMD, Germany) David Madigan (University of Washington, USA) Chris Matheus (GTE Laboratories, USA) Heikki Mannila (University of Helsinki, Finland) Sham Navathe (Georgia Institute of Technology, USA) Raymond Ng (University of British Columbia, Canada) Gregory Piatetsky-Shapiro (GTE Laboratories, USA) Daryl Pregibon (AT&T Bell Laboratories, USA) Ted Senator (US Department of the Treasury, USA) Wei-Min Shen (University of Southern California, USA) Arno Siebes (CWI, Netherlands) Andrzej Skowron (University of Warsaw, Poland) Steve Smith (Dun and Bradstreet, USA) Padhraic Smyth (Jet Propulsion Laboratory, USA) Ramakrishnan Srikant (IBM Almaden Research Center, USA) Alex Tuzhilin (NYU Stern School, USA) Ramasamy Uthurusamy (GM Research Laboratories, USA) Xindong Wu (Monash University, Australia) Wojciech Ziarko (University of Regina, Canada) Jan Zytkow (Wichita State University, USA) For further information, send inquiries regarding * submission logistics to AAAI at kdd@aaai.org Phone: (+1 415) 328-3123; Fax: (+1 415) 321-4457 * KDD-96 sponsorship and industry participation to: Gregory Piatetsky-Shapiro at gps@gte.com Phone: 617-466-4236, Fax: 617-466-2960 * technical program and content to kdd96@almaden.ibm.com * general and publicity issues to kdd96@aig.jpl.nasa.gov ------------------------------ From: mccalla@cs.usask.ca Subject: AI 96 - Deadline Approaches Date: Fri, 6 Oct 1995 17:11:12 -0600 AI 96 - Canadian Artificial Intelligence Conference A reminder that the paper submission deadline for AI 96, the Eleventh Biennial Conference of the Canadian Society for Computational Studies of Intelligence, is rapidly approaching. The conference will be held in Toronto, Ontario, Canada, from May 21-24, 1996. Two related conferences, Vision Interface 96, and Graphics Interface 96, will be held at the same time and in the same place. Papers are solicited in all areas of Artificial Intelligence. Each paper will be reviewed by a high quality program committee. Here are some important deadlines: Paper submission deadline: October 31, 1995 (4 copies, less than 5000 words) Notification of acceptance/rejection: Janaury 31, 1996 Camera ready copy: March 28, 1996 Send papers to Gord McCalla, Program Chair Dept. of Computer Science University of Saskatchewan 57 Campus Drive Saskatoon, Saskatchewan S7N 5A9 CANADA telephone: (306) 966-4902 fax: (306) 966-4884 e-mail: mccalla@cs.usask.ca web: http://ai.iit.nrc.ca/cscsi/conferences/ai96.html ------------------------------ From: Sandip Sen Subject: 2nd CFP: AAAI'96 Symposium on Multiagent Learning Date: Fri, 13 Oct 95 12:22:26 CDT AAAI-96 Spring Symposium Series March 25-27, 1996 Stanford University, California Adaptation, Co-evolution and Learning in Multiagent Systems =========================================================== Coordination of multiple agents is essential for the viability of systems in which these agents share resources. Most of the research in Distributed Artificial Intelligence have concentrated on developing coordination strategies off-line. These pre-fabricated strategies can quickly become inadequate if the system designer's world model is incomplete/incorrect or if the environment can change dynamically. Learning and adaptation are invaluable mechanisms by which agents can evolve coordination strategies that meet the demands of the environments and the requirements of individual agents. The goal of this symposium is to focus on research that will address unique requirements for agents learning and adapting to work with other agents. Recognizing the applicability and limitations of current machine learning research as applied to multiagent problems as well as developing new learning and adaptation mechanisms particularly targeted to these class of problems will be of particular relevance to this symposium. We would particularly welcome new insights into this class of problems from other related disciplines, and thus would like to emphasize the inter-disciplinary nature of the symposium. Among others, papers of the following kind are welcome: -- Benefits of adaptive/learning agents over agents with fixed behavior in multiagent problems. -- Characterization of methods in terms of modeling power, communication abilities, and knowledge requirement of individual agents. -- Developing learning and adaptation strategies for environments with cooperative agents, selfish agents, partially cooperative agents. -- Analyzing and constructing algorithms that guarantee convergence and stability of group behavior. -- Co-evolving multiple agents with similar/opposing interests. -- Inter-disciplinary research from fields like organizational theory, game theory, psychology, sociology, economics, etc. Submission information ###################### The symposium will consist of individual presentations, invited talks, break-out group discussions, panels, and video sessions. Participants interested in presenting their work should send an extended abstract (12 point font, 5 pages or less) describing work in progress or completed work. Other interested participants should send a one-page description of their research interests with a short list of relevant publications. We would like to encourage submissions for video presentations and for working systems that can be used for hands-on demonstration during the symposium. We will accept only e-mail submissions of postscript files. Submissions should be sent to sandip@kolkata.mcs.utulsa.edu. Further information on this symposium can be found on the WWW at http://euler.mcs.utulsa.edu/~sandip/ss.html. Submissions for the symposia are due on October 31, 1995. Notification of acceptance will be given by November 30, 1995. Material to be included in the working notes of the symposium must be received by January 19, 1996. Organizing Committee #################### Sandip Sen (Chair), University of Tulsa, sandip@kolkata.mcs.utulsa.edu Devika Subramanian, Cornell University, devika@CS.Cornell.EDU Jeff Rosenschein, The Hebrew University, jeff@CS.HUJI.AC.IL John J. Grefenstette, Naval Research Laboratory, gref@AIC.NRL.Navy.Mil Michael N. Huhns, University of South Carolina, huhns@sc.edu Tad Hogg, Xerox PARC, hogg@parc.xerox.com ------------------------------ From: Yolanda Gil Subject: Acquisition, Learning, and Demonstration: Automating Tasks for Users Date: Tue, 3 Oct 1995 15:32:41 -0700 CALL FOR PAPERS: 1996 AAAI SPRING SYMPOSIUM "Acquisition, Learning, and Demonstration: Automating Tasks for Users" March 25-27, 1996 Stanford, CA Automating tasks through interactions with users has always been recognized as an important area of research, one that will attract increasing attention in the next few years. Larger bodies of knowledge will need to be acquired and maintained as AI systems are scaled up and applied to real-world problems. The interactive nature of the growing Internet (where many services will be offered and diverse intelligent assistants created) will pose an increasing demand on tools that help users define tasks they want computers to accomplish for them. Currently, researchers in three different communities are looking at different aspects of this problem. Machine Learning researchers tend to look for ways to automate the acquisition process with algorithms that do explanation or induction based on a user's actions. Knowledge Acquisition research, mainly motivated by the automation of knowledge-intensive tasks, concentrates on understanding how to structure the system's interaction with users based on the nature of the task to be automated. The area of Programming by Demonstration, which emerged from the user interface and human-computer interaction communities, offers more natural ways for non-programmers to automate tasks using systems that analyze the sequence of actions chosen by a user to perform a task. The main purpose of this symposium is to bring together these communities in order to exchange ideas and approaches, and to gain a better understanding of the state of the art and the technological and research challenges that we need to address in getting computers to do intelligent tasks for users. Possible discussion topics for this symposium include: - Combining existing approaches: Which aspects of a task can be learned automatically and how? Can we handle with current induction techniques the iterative constructs required for tasks typical of demonstration systems? When and how can we generalize from a few examples provided by a user? Are the problem-solving methods that have been identified by the knowledge acquisition community useful in knowledge-lean tasks? - Analysis and methodology issues: What kinds of knowledge are hard to acquire and why? Can we characterize classes of tasks that users can automate easily? How can computers interact better with users in specific contexts? How much of the task should the system model and understand? - Practical concerns: What challenging tasks would users like to automate? What do current approaches offer for these tasks? How do we address the tradeoff between burdening the user and autonomy? How scalable are the proposed methods? The first day of the symposium will include short tutorial-style presentations to provide a shared background of the approaches and applications in Machine Learning, Knowledge Acquisition, and Programming by Demonstration. The rest of the sessions will center around a few papers representing current approaches and open issues within each area and across areas. Each presentation will be followed by a discussion prepared by people with an alternative research focus, and finally, by an open discussion. Based on the submissions received, we will select a set of sample tasks that will be suggested to the participants to guide the preparation of the final papers and the discussions during the symposium. SUBMISSION INFORMATION Potential participants should submit a short paper (two to eight pages) with a crisp description of open questions in interactive task automation, solutions presented by a specific approach, or lessons learned from a research project. Send PostScript or hard-copy submissions by October 31, 1995 to: Yolanda Gil Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA 90045 (310) 822-1511 gil@isi.edu UPDATED ONLINE INFORMATION About the AAAI Spring Symposium Series: http://aaai.org/Symposia/symposia.html About this symposium: http://www.atg.apple.com/Allen_Cypher/ALD/CFP.html ORGANIZING COMITTEE William P. Birmingham, University of Michigan at Ann Arbor; Allen Cypher, Apple Computer; Yolanda Gil (chair), USC/Information Sciences Institute, gil@isi.edu; Mike Pazzani, University of California at Irvine. IMPORTANT DATES: October 31, 1995 Submission deadline November 30, 1995 Notification of acceptance February 2, 1996 Final papers due March 25-27, 1996 Spring Symposium in Stanford, CA ------------------------------ From: minton@isi.edu Subject: ML-related JAIR articles Date: Thu, 28 Sep 95 12:32:11 PDT Readers of this group may be interested in the following ML-related papers recently published in JAIR. Instructions for obtaining JAIR articles are given below. Schaerf, A., Shoham, Y. and Tennenholtz, M. (1995) "Adaptive Load Balancing: A Study in Multi-Agent Learning", Volume 2, pages 475-500. postscript: volume2/schaerf95a.ps (265K) compressed, volume2/schaerf95a.ps.Z (107K) Abstract: We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency. Cohen, W.W. (1995a) "Pac-Learning Recursive Logic Programs: Efficient Algorithms", Volume 2, pages 501-539. PostScript: volume2/cohen95a.ps (339K) compressed, volume2/cohen95a.ps.Z (135K) Abstract: We present algorithms that learn certain classes of function-free recursive logic programs in polynomial time from equivalence queries. In particular, we show that a single k-ary recursive constant-depth determinate clause is learnable. Two-clause programs consisting of one learnable recursive clause and one constant-depth determinate non-recursive clause are also learnable, if an additional ``basecase'' oracle is assumed. These results immediately imply the pac-learnability of these classes. Although these classes of learnable recursive programs are very constrained, it is shown in a companion paper that they are maximally general, in that generalizing either class in any natural way leads to a computationally difficult learning problem. Thus, taken together with its companion paper, this paper establishes a boundary of efficient learnability for recursive logic programs. Cohen, W.W. (1995b) "Pac-learning Recursive Logic Programs: Negative Results", Volume 2, pages 541-573. PostScript: volume2/cohen95b.ps (342K) compressed, volume2/cohen95b.ps.Z (136K) Abstract: In a companion paper it was shown that the class of constant-depth determinate k-ary recursive clauses is efficiently learnable. In this paper we present negative results showing that any natural generalization of this class is hard to learn in Valiant's model of pac-learnability. In particular, we show that the following program classes are cryptographically hard to learn: programs with an unbounded number of constant-depth linear recursive clauses; programs with one constant-depth determinate clause containing an unbounded number of recursive calls; and programs with one linear recursive clause of constant locality. These results immediately imply the non-learnability of any more general class of programs. We also show that learning a constant-depth determinate program with either two linear recursive clauses or one linear recursive clause and one non-recursive clause is as hard as learning boolean DNF. Together with positive results from the companion paper, these negative results establish a boundary of efficient learnability for recursive function-free clauses. Mooney, R.J. and Califf, M.E. (1995) "Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs", Volume 3, pages 1-24. PostScript: volume3/mooney95a.ps (252K) compressed, volume3/mooney95a.ps.Z (102K) Abstract: This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut. The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as learning the past-tense of English verbs, a task widely studied in the context of the symbolic/connectionist debate. FOIDL is able to learn concise, accurate programs for this problem from significantly fewer examples than previous methods (both connectionist and symbolic). Bergmann, R. and Wilke, W. (1995) "Building and Refining Abstract Planning Cases by Change of Representation Language", Volume 3, pages 53-118. PostScript: volume3/bergmann95a.ps (850K) compressed, volume3/bergmann95a.ps.Z (302K) Online Appendix: volume3/bergmann95a-appendix (11K) data file Abstract: Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description -- as used in most hierarchical planners -- has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of Paris (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning. Giraud-Carrier, C.G. and Martinez, T.R. (1995) "An Integrated Framework for Learning and Reasoning", Volume 3, pages 147-185. PostScript: volume3/giraud-carrier95a.ps (375K) compressed, volume3/giraud-carrier95a.ps.Z (152K) Online Appendix: volume3/giraud-carrier95a-appendix.tar (90K) code/data Abstract: Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems. The PostScript files are available via: -- World Wide Web: The URL for our World Wide Web server is http://www.cs.washington.edu/research/jair/home.html -- comp.ai.jair.papers -- Anonymous FTP from either of the two sites below: CMU: p.gp.cs.cmu.edu directory: /usr/jair/pub/volume2 Genoa: ftp.mrg.dist.unige.it directory: pub/jair/pub/volume2 -- automated email. Send mail to jair@cs.cmu.edu or jair@ftp.mrg.dist.unige.it with the subject AUTORESPOND, and the body GET VOLUME2/ORTEGA95A.PS or GET VOLUME2/TURNEY95A.PS or GET VOLUME2/DONOHO95A.PS (either upper or lowercase is fine). Note: Your mailer might find these files too large to handle. -- JAIR Gopher server: At p.gp.cs.cmu.edu, port 70. For more information about JAIR, check out our WWW or FTP sites, or send electronic mail to jair@cs.cmu.edu with the subject AUTORESPOND and the message body HELP, or contact jair-ed@ptolemy.arc.nasa.gov. ------------------------------ Subject: ICML-96: Final Call for Papers Date: Fri, 13 Oct 1995 12:05:38 -0700 (PDT) From: "Jeffrey C. Schlimmer" ************************************************** ICML'96 13th International Conference on Machine Learning Bari (Italy), July 3-6th, 1996 ************************************************** Call for Papers and Workshop Proposals ************************************************** General Information ___________________ The 13th International Conference on Machine Learning (ICML'96) will be held in Bari, Italy, during July 3-6th, 1996, with informal workshops on July 3rd. The purpose of the conference is twofold: firstly, to emphasize the potential of machine learning approaches for solving problems in a wide range of application domains, secondly, to highlight relationships between machine learning and other fields, such as statistics, pattern recognition, artificial intelligence, information retrieval, instructional and cognitive sciences, software engineering. In order to stress the multidisciplinary character of the field and the large spectrum of possible application domains, the Program Committe has been widened to include also experts from fields related to or interested in Machine Learning. The Conference on Computational Learning Theory (COLT'96) will be also held in Italy, namely in Desenzano sul Garda, on June 28th-July 1st, 1996. Program _______ The scientific program will include invited talks, presentations of refereed papers and a session of general discussion. Submissions are invited in all areas of Machine Learning, including, but not limited to: Abduction Analogy Applications of machine learning Artificial neural networks Case-Based learning Cognitive models of learning Computational learning theory Explanation-based learning Formal models of learning Inductive learning Inductive logic programming Genetic algorithms Knowledge discovery in databases Learning and problem solving Multistrategy learning Reinforcement learning Representation change Scientific discovery Theory revision Paper Format ____________ Submissions must be clearly legible, with good quality print. Papers are limited to twelve (12) pages, excluding title page and bibliography, but including all tables and figures. Papers must be printed on 8-1/2 x 11 inch paper or A4 format, using 12 point type (10 characters per inch), with no more than 40 lines per page. A separate title page must include the title of the paper, the email and postal addresses of all authors, up to three keywords, and a clear summary of the main contributions of the paper. The title page of accepted papers will be made available via World-Wide Web before the conference take place. Double-sided printing in encouraged. Requirement for Submissions ___________________________ Please send five (5) copies of each submitted paper to the Program Chair. Submissions must be received by January 21st, 1996. Electronic or Fax submissions are not acceptable. Notification of acceptance or rejection will be mailed to the first (or designated) author by March 8th 1996. Camera-ready accepted papers are due on April 6th, 1996. Review Criteria _______________ Each submitted paper will be reviewed by three members of the Program or Advisory Committee, and will be judged on significance, originality and clarity. Papers addressing application issues are welcome. Simultaneous submission to other conferences must be explicitly declared. In the case of multiple acceptance, presentation at ICML'96 and inclusion in the proceedings is only granted upon withdrawal from the other conference(s). Workshop Proposals __________________ Workshop proposals are invited in all areas of Machine Learning. Please send a two (2) page description of the proposed workshop, its objectives, organizer(s), and expected number of attendees. The proposal must be received by the Workshop Chair by December 15th, 1995. Descriptions of accepted workshops will be made available via World-Wide Web. Notification of acceptance or rejection will be mailed to the organizer by January 31st, 1996. Calls for Papers for accepted workshops will be responsibility of the organizer(s). Program Chair _____________ Lorenza Saitta saitta@di.unito.it Universita di Torino Phone: (+39) 11 - 7429.214 Dipartimento di Informatica Fax: (+39) 11 - 751.603 Corso Svizzera 185 10149 Torino (Italy) Local Chair ___________ Floriana Esposito esposito@vm.csata.it Universita di Bari Phone: (+39) 80 - 5443.264 Dipartimento di Informatica Fax: (+39) 80 - 5443.196 Via Orabona 4 70125 Bari (Italy) Workshop Chair ______________ Stefan Wrobel (wrobel@gmdzi.gmd.de) GMD, FIT.KI Schlo Birlinghoven 53754 Sankt Augustin (Germany) Publicity Chair _______________ Jeff Schlimmer (schlimme@eecs.wsu.edu) School of Electrical Engineering and Computer Science Washington State University Pullman, WA 99164-2752 (USA) Advisory Committee __________________ Jaime Carbonell (Carnegie Mellon University, USA) William Cohen (AT&T Bell Laboratories, USA) Kenneth De Jong (George Mason University, USA) Tom Dietterich (Oregon State University, USA) Tom Mitchell (Carnegie Mellon University, USA) Stuart Russell (University of California at Berkeley, USA) Derek Sleeman (University of Aberdeen, UK) Paul Utgoff (University of Massachusetts, USA) Program Committee _________________ Naoki Abe (C&C Research Laboratory, Japan) Christopher Atkeson (Georgia Tech, USA) Wolfgang Banzhaf (University of Dortmund, Germany) Andrew Barto (University of Massachusetts, USA) Francesco Bergadano (Universita di Torino, Italy) Ivan Bratko (University of Ljubljana, Slovenia) Carla Brodley (Purdue University, USA) Jason Catlett (AT&T Bell Laboratories, USA) Eugene Charniak (Brown University, USA) Bruce Croft (University of Massachusetts, USA) Peter Dayan (MIT, USA) Gerald DeJong (University of Illinois at Urbana, USA) Luc De Raedt (Katholieke Universiteit Leuven, Belgium) Yves Deville (Universite Catholique de Louvain, Belgium) Charles Elkan (University of California at San Diego, USA) Tom Ellman (Rutgers University, USA) Usama Fayyad (California Institute of Technology, USA) Nick Flann (Utah State University, USA) Paolo Fortina (Children's Hospital of Philadelphia, USA) John Grefenstette (Naval Research Laboratory, USA) Russell Greiner (Siemens Corporate Research, USA) David Hand (The Open University, UK) Michael Jordan (MIT, USA) Leslie Kaelbling (Brown University, USA) Yves Kodratoff (Universitee de Paris-Sud, France) Kurt Konolige (SRI International, USA) Wolfgang Maass (Teknische Universitat Graz, Austria) David MacKay (Cavendish Laboratory, UK) Donato Malerba (Universita di Bari, Italy) Chris Matheus (GTE Laboratories, USA) Ray Mooney (University of Texas, USA) Katharina Morik (University of Dortmund, Germany) Hiroshi Motoda (Hitachi Ltd., Japan) Michael Pazzani (University of California at Irvine, USA) Daryl Pregibon (AT&T Bell Laboratories, USA) Armand Prieditis (University of California at Davis, USA) Peter Reimann (University of Freiburg, Germany) Claude Sammut (University of New South Wales, Australia) Robert Schapire (AT&T Bell Laboratories, USA) Devika Subramanian (Rice University, USA) Prasad Tadepalli (Oregon State University, USA) Sebastian Thrun (Carnegie Mellon University, USA) Naftali Tishby (The Hebrew University, Israel) Raul Valdes-Perez (Carnegie Mellon University, USA) Vladimir Vapnik (AT&T Bell Laboratories, USA) Manuela Veloso (Carnegie Mellon University, USA) Organizing Committee ____________________ Giovanni Semeraro (Italy) semeraro@vm.csata.it Marco Botta and Filippo Neri (Italy) {botta, neri}@di.unito.it General Inquiries _________________ Please address general inquiries to any of the members of the Organizing Committee or to the address: icml96@di.unito.it ICML'96 has its own page on the World-Wide Web in the URL at: http://www.di.unito.it/pub/WWW/ICML96/home.html This announcement is also available in PostScript in the URL at: ftp://ftp.di.unito.it/pub/ICML96/callforpapers.ps In order to be included in the mailing list, please send a note to the Publicity Chair. Important dates ________________ Workshop submission deadline: December 15, 1995 Paper submission deadline: January 21, 1996 Notification of workshop acceptance: January 31, 1996 Notification of paper acceptance: March 8, 1996 Camera-ready copy: April 6, 1996 ****************************************************************************** ------------------------------ End of ML-LIST (Digest format) **************************************** From nin@cns.brown.edu Mon Oct 16 05:37:56 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 16 Oct 95 05:37:54 -0500; AA12733 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Mon, 16 Oct 95 05:37:51 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa04021; 15 Oct 95 16:57:32 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa04019; 15 Oct 95 16:39:08 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa02427; 15 Oct 95 16:38:15 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa01921; 13 Oct 95 12:21:10 EDT Received: from cns.brown.edu by RI.CMU.EDU id aa05386; 13 Oct 95 12:19:56 EDT Received: by cns.brown.edu (4.1/1.00) id AA12898; Fri, 13 Oct 95 12:03:52 EDT Date: Fri, 13 Oct 95 12:03:52 EDT From: Nathan Intrator Message-Id: <9510131603.AA12898@cns.brown.edu> To: Connectionists@cs.cmu.edu Subject: NIPS Workshop: Object Features for Visual Shape Representation Cc: Shimon Edelman , nin@math.tau.ac.il First announcement of the following workshop. Updates and call for presentations is on the web page. ---------------------------------------------------------------------- Object Features for Visual Shape Representation NIPS-95 Workshop: Saturday, Dec 2, 1995 Organizers: Shimon Edelman, Nathan Intrator http://www.physics.brown.edu/~nin/workshop95.html Overview Object recognition can be regarded as a comparison between the stimulus shape and a library of reference shapes stored in long-term memory. It is not likely that the visual system stores exact templates or snapshots of familiar objects, both for pragmatic reasons (the appearance of a 3D object depends on the viewing conditions, making a close match between the stimulus and a template unattainable), and because of computational limitations that have to do with the curse of dimensionality. Many competing approaches to the extraction of features useful for shape representation have been proposed in the past. The workshop will explore and compare some of these approaches. We shall be particularly interested in discussing different approaches for evaluating feature extraction rules: information theory, statistics, pattern recognition etc. We would like to elaborate on the goal of feature/information extraction in early visual cortex, the relevance of the statistics of the input environment to studying learning rules and comparison between visual cortical plasticity models. Presentation of psychophysical and neurobiological data relevant to the feature issue will be encouraged. POTENTIAL PARTICIPANTS: connectionists / feature extraction people / vision researchers / neurobiologists working on perceptual learning Invited Speakers ---------------- Joseph Atick, Rockefeller (Tentative) Horace Barlow, Cambridge (Tentative) Elie Binenstock, CNRS, Brown Ichiro Fujita, Osaka Stu Geman, Brown Tai Sing Lee, Harvard Bruno A. Olshausen, Cornell Tommy Poggio, MIT Dan Ruderman, USC (Tentative) Harel Shouval, Brown From tony@discus.anu.edu.au Mon Oct 16 14:13:27 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 16 Oct 95 14:13:17 -0500; AA20886 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Mon, 16 Oct 95 14:13:15 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa05048; 16 Oct 95 10:20:49 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa05046; 16 Oct 95 10:10:27 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04068; 16 Oct 95 10:09:34 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa05627; 16 Oct 95 2:55:37 EDT Received: from anugpo.anu.edu.au by CS.CMU.EDU id aa07964; 16 Oct 95 2:54:49 EDT Received: from cslab.anu.edu.au (discus.anu.edu.au [150.203.126.185]) by anugpo.anu.edu.au (8.6.12/8.6.12) with ESMTP id QAA19726 for ; Mon, 16 Oct 1995 16:53:01 +1000 Received: by cslab.anu.edu.au (SMI-8.6/SMI-SVR4) id QAA04463; Mon, 16 Oct 1995 16:52:50 +1000 Date: Mon, 16 Oct 1995 16:52:50 +1000 Message-Id: <199510160652.QAA04463@cslab.anu.edu.au> From: Tony BURKITT To: Connectionists@cs.cmu.edu Subject: ACNN'96: Call for Papers C A L L F O R P A P E R S ACNN'96 SEVENTH AUSTRALIAN CONFERENCE ON NEURAL NETWORKS 10th - 12th APRIL 1996 Australian National University Canberra, Australia The seventh Australian conference on neural networks will be held in Canberra on April 10th - 12th 1996 at the Australian National University. ACNN'96 is the annual national meeting of the Australian neural network community. It is a multi-disciplinary meeting and seeks contributions from Neuroscientists, Engineers, Computer Scientists, Mathematicians, Physicists and Psychologists. ACNN'96 will feature a number of invited speakers. The program will include lecture presentations and poster sessions. Proceedings will be printed and distributed to the attendees. The posters will be displayed for a significant period of time, and time will be allocated for authors to be present at their poster in the conference program. Pre-Conference Workshops and Tutorials Proposals for Pre-Conference Workshops and Tutorials are invited. These are to be held on Tuesday 9th April at the same venue as the conference. People wishing to organize such workshops or tutorials are invited to submit a precis at the same time as the submission deadline for papers, and these will be advertised. Invited Keynote Speakers ACNN'96 will feature a number of keynote speakers, including Professor Wolfgang Maass, Technical University Graz. Further details to be announced. Submission Categories The major categories for paper submissions include: 1. Computational Neuroscience: Integrative function of neural networks in Vision, Audition, Motor, Somatosensory and Autonomic functions; Synaptic function; Cellular information processing; 2. Theory: Learning; Generalisation; Complexity; Scaling; Stability; Dynamics; 3. Implementation: Hardware implementation of neural nets; Analog and digital VLSI implementation; Optical implementation; 4. Architectures and Learning Algorithms: New architectures and learning algorithms; Hierarchy; Modularity; Learning pattern sequences; Information integration; 5. Cognitive Science and AI: Computational models of perception and pattern recognition; Memory; Concept formation; Problem solving and reasoning; Visual and auditory attention; Language acquisition and production; Neural network implementation of expert systems; 6. Applications: Application of neural nets to signal processing and analysis; Pattern recognition: Speech, Machine vision; Motor control; Robotics; Forecasting; Medical. Initial Submission of Papers As this is a multi-disciplinary meeting, papers are required to be comprehensible to an informed researcher outside the particular stream of the author in addition to the normal requirements of technical merit. Papers should be submitted as close as possible to final form and must not exceed six single A4 pages (2-column format). A cover page should be supplied giving the title of the paper, the name and affiliation of each author, together with the postal address, the e-mail address, and the phone and fax numbers of a designated contact author. The type font should be no smaller than 10 point except in footnotes. A serif font such as Times or New Century Schoolbook is preferred. . A LaTeX style file and a LaTeX template file specifying the final format are available by ftp (in the directory ftp://syseng.anu.edu.au/pub/acnn96/paperformat). Five copies of the paper and the front cover sheet should be sent to: ACNN'96 Secretariat L.P.O. Box 228 Australian National University Canberra, ACT 2601 Australia Each manuscript should clearly indicate submission category (from the six listed) and author preference for oral or poster presentations. This initial submission must be on hard copy to reach us by Friday, 1 December 1995. ACNN'96 will include a special poster session devoted to recent work and work-in-progress. Abstracts are solicited for this session (1 page limit), and may be submitted up to one week before the commencement of the conference. They will not be refereed or included in the proceedings, but will be distributed to attendees upon arrival. Students are especially encouraged to submit abstracts for this session. Submission Deadlines Friday, 1 December 1995 Deadline for receipt of paper submissions Friday, 19 January 1996 Notification of acceptance Friday, 16 February 1996 Final papers in camera-ready form for printing Venue Huxley Lecture Theatre, Leonard Huxley Building, Mills Road, Australian National University, Canberra, Australia. ACNN'96 Organising Committee Peter Bartlett Australian National University Tony Burkitt Australian National University Bob Williamson Australian National University ACNN'96 Technical Program Committee Tom Downs University of Queensland Bill Gibson University of Sydney Andrew Heathcote University of Newcastle Marwan Jabri University of Sydney Adam Kowalczyk Telecom Research Laboratories Cyril Latimer University of Sydney Wee Sun Lee Australian National University M. V. Srinivasan Australian National University Registrations The registration fee to attend ACNN'96 is: Full Time Students A$120.00 Academics A$260.00 Other A$380.00 A discount of 20% applies for advance registration. Registration forms must be posted before February 16th, 1996, to be entitled to the discount. To be eligible for the Full Time Student rate, a letter from the Head of Department as verification of enrollment is required. There is a registration form at the end of this document. Accommodation Delegates will have to make their own accommodation arrangements directly with the college or hotel of their choice. A list of accomodation close to the conference venue is available (see "Further Information"). Further Information For further information and registration forms, contact: ACNN'96 Secretariat L.P.O. Box 228 Australian National University Canberra, ACT 2601 Australia Tel: 06 - 249 5645 WWW page: http://wwwsyseng.anu.edu.au/acnn96/ ftp site: syseng.anu.edu.au:pub/acnn96 or via email: acnn96@anu.edu.au From wilson@smith.rowland.org Wed Oct 18 10:23:30 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 18 Oct 95 10:23:23 -0500; AA27437 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Wed, 18 Oct 95 10:23:13 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id NAA27561; Wed, 18 Oct 1995 13:16:27 +0800 Message-Id: <199510180516.NAA27561@cs.uwa.oz.au> From: Stewart Wilson To: reinforce@cs.uwa.edu.au Subject: Paper on Generalization Date: Tue, 17 Oct 1995 16:59:24 -0400 Since generalization is currently a big issue in reinforcement learning, readers of the list may be interested in the following paper, which exhibits generalization in RL, but via a classifier system. Or I will be happy to post you a hard copy reprint if you send your address to wilson@smith.rowland.org. "Classifier Fitness Based on Accuracy" Evolutionary Computation 3(2) 149-175 Stewart W. Wilson The Rowland Institute for Science 100 Edwin H. Land Blvd. Cambridge, MA 02142 ABSTRACT In many classifier systems, the classifier strength parameter serves both as a predictor of future payoff and as the classifier's fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier's fitness is given by a measure of the prediction's accuracy. The system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its population tending to form a complete and accurate mapping X x A => P from inputs and actions to payoff predictions. Further, XCS tends to evolve classifiers that are maximally general, subject to an accuracy criterion. Besides introducing a new direction for classifier system research, these properties of XCS make it suitable for a wide range of reinforcement learning situations where generalization over states is important. From wilson@smith.rowland.org Thu Oct 19 10:27:52 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 19 Oct 95 10:27:50 -0500; AA15301 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Thu, 19 Oct 95 10:27:40 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id OAA01264; Thu, 19 Oct 1995 14:12:08 +0800 Message-Id: <199510190612.OAA01264@cs.uwa.oz.au> From: Stewart Wilson To: reinforce@cs.uwa.edu.au Subject: Addemdum--Paper on Generalization Date: Wed, 18 Oct 1995 13:02:24 -0400 In yesterday's message I somehow dropped the line: "The paper can be downloaded from http://netq.rowland.org/sw/swhp.html" I will send hard copy anyway to those who already contacted me (unless you tell me otherwise). Sorry for the extra traffic on the list! -Stewart Wilson For completeness, here is yesterday's message: Since generalization is currently a big issue in reinforcement learning, readers of the list may be interested in the following paper, which exhibits generalization in RL, but via a classifier system. Or I will be happy to post you a hard copy reprint if you send your address to wilson@smith.rowland.org. "Classifier Fitness Based on Accuracy" Evolutionary Computation 3(2) 149-175 Stewart W. Wilson The Rowland Institute for Science 100 Edwin H. Land Blvd. Cambridge, MA 02142 ABSTRACT In many classifier systems, the classifier strength parameter serves both as a predictor of future payoff and as the classifier's fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier's fitness is given by a measure of the prediction's accuracy. The system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its population tending to form a complete and accurate mapping X x A => P from inputs and actions to payoff predictions. Further, XCS tends to evolve classifiers that are maximally general, subject to an accuracy criterion. Besides introducing a new direction for classifier system research, these properties of XCS make it suitable for a wide range of reinforcement learning situations where generalization over states is important. From pazzani@super-pan.ICS.UCI.EDU Sat Oct 21 14:48:37 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Sat, 21 Oct 95 14:48:34 -0500; AA20916 Received: from paris.ics.uci.edu by lucy.cs.wisc.edu; Sat, 21 Oct 95 14:48:24 -0500 Received: from super-pan.ics.uci.edu by paris.ics.uci.edu id aa04907; 21 Oct 95 10:30 PDT To: ML-LIST:; Subject: Machine Learning List: Vol. 7, No. 18 Reply-To: ml@ics.uci.edu Date: Sat, 21 Oct 1995 10:16:18 -0700 From: Michael Pazzani Message-Id: <9510211030.aa04907@paris.ics.uci.edu> Machine Learning List: Vol. 7, No. 18 Saturday, October 21, 1995 Contents: 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 ---------------------------------------------------------------------- Subject: PhD Dissertation by Ron Kohavi available From: Ronny Kohavi Date: Sun, 15 Oct 1995 20:29:57 -0700 Wrappers for Performance Enhancement and Oblivious Decision Graphs Ron Kohavi Stanford University http://robotics.stanford.edu/~ronnyk We study three basic problems in machine learning and two new hypothesis spaces with corresponding learning algorithms. The problems we investigate are: accuracy estimation, feature subset selection, and parameter tuning. The latter two problems are related and are studied under the wrapper approach. The hypothesis spaces we investigate are: decision tables with a default majority rule DTMS and oblivious read-once decision graphs OODGs. For accuracy estimation, we investigate cross-validation and the .632 bootstrap. We show examples where they fail and conduct a large scale study comparing them. We conclude that repeated runs of five-fold cross-validation give a good tradeoff between bias and variance for the problem of model selection used in later chapters. We define the WRAPPER APPROACH and use it for feature subset selection and parameter tuning. We relate definitions of feature relevancy to the set of optimal features, which is defined with respect to both a concept and an induction algorithm. The wrapper approach requires a search space, operators, a search engine, and an evaluation function. We investigate all of them in detail and introduce COMPOUND OPERATORS for feature subset selection. Finally, we abstract the search problem into search with probabilistic estimates. We introduce decision tables with a default majority rule DTMs to test the conjecture that feature subset selection is a very powerful bias. The accuracy of induced DTMs is surprisingly powerful, and we concluded that this bias is extremely important for many real-world datasets. We show that the resulting decision tables are VERY small and can be succinctly displayed. We study properties of oblivious read-once decision graphs OODGs and show that they do not suffer from some inherent limitations of decision trees. We describe a a general framework for constructing OODGs bottom-up and specialize it using the wrapper approach. We show that the graphs produced are use less features than C4.5, the state-of-the-art decision tree induction algorithm, and are usually easier for humans to comprehend. To retrieve, either click on the "online" dissertation off my home page http://robotics.stanford.edu/~ronnyk or use anonymous ftp to ftp://starry.stanford.edu/pub/ronnyk/teza.ps. The dissertation is 281 pages (4.1MB) and includes an index. ------------------------------ Subject: new WWW page on integrated and hybrid models From: Vasant Honavar Date: Tue, 17 Oct 1995 11:31:32 CDT The following WWW page might be of interest to some members of this list: http://www.cs.iastate.edu/~honavar/hybrid-ai.html Regards, Vasant Honavar honavar@cs.iastate.edu http://www.cs.iastate.edu/~honavar/homepage.html ------------------------------ Subject: New pointers for Rich Sutton From: Rich Sutton Date: Wed, 18 Oct 1995 10:41:00 -0500 I've moved! This is to let you know that I have left GTE Labs and am now at the University of Massachusetts. My old gte email address will stop working soon. I can be reached at: Rich Sutton Department of Computer Science University of Massachusetts Amherst, MA 01003 USA rich@cs.umass.edu http://envy.cs.umass.edu/People/sutton/sutton.html I am also looking for a few good students. UMass has an excellent program in artificial intelligence with large efforts in vision, robotics, cognitive modeling, and reinforcement learning, among many others. Also brain/behavior links. Check us out on the web. p.s. My ftp site at GTE is being closed -- please update any pointers. I'll be out of town and unreachable for the next four weeks. ------------------------------ Subject: GRADUATE STUDY: JOHNS HOPKINS CENTER FOR LANG. AND SPEECH PROCESSING From: Eric Brill Date: Wed, 18 Oct 1995 13:39:25 -0400 CENTER FOR LANGUAGE AND SPEECH PROCESSING Johns Hopkins University Baltimore, Md. The Center for Language and Speech Processing (CLSP) at Johns Hopkins University encourages students interested in pursuing a graduate degree in any aspect of language and speech processing to apply to Johns Hopkins. Graduate students interested in language and speech processing who want to conduct research at CLSP must first be admitted to a graduate program in one of the various departments that have CLSP faculty. Students must meet the requirements for admission and degree completion in their home department. To obtain an application to any of the affiliated departments, send mail to the address provided below. Be sure to indicate which department(s) you wish to apply to. Home Departments and Selected Faculty Cognitive Science MICHAEL BRENT, Ph.D., MIT, 1991. Computational models of language acquisition, machine learning of natural language. LUIGI BURZIO, Ph.D., MIT, 1981. Theories of syntax and phonology, rules versus constraints in lexical organization. ROBERT FRANK, Ph.D., University of Pennsylvania, 1992. Natural language syntax and foundations of grammatical theory, tree adjoining grammars, computational and empirical studies of language acquisition and language processing. PAUL SMOLENSKY, Ph.D., Indiana University, 1981. Integration of neural/connectionist and symbolic computation, soft constraints in universal grammar, optimality theory, phonology and syntax. Computer Science ERIC BRILL, Ph.D., University of Pennsylvania, 1993. Natural language and speech processing, machine learning, artificial intelligence. SIMON KASIF, Ph.D., University of Maryland, 1985. Artificial intelligence, parallel computation, machine learning, computational modeling. STEVEN SALZBERG, Ph.D., Harvard, 1989. Machine learning, computational biology, pattern recognition. DAVID YAROWSKY, Ph.D., University of Pennsylvania, 1995. Natural language processing and spoken language systems, machine translation, information retrieval and machine learning. Electrical and Computer Engineering ANDREAS ANDREOU, Ph.D., Johns Hopkins University, 1986. Sensory communication, acoustic processing for speech recognition using models of audition and speech production, low power analog VLSI auditory models. GERT CAUWENBERGHS, Ph.D., California Institute of Technology, 1994. Neural networks, model free learning, low power integrated circuits for speech encoding/decoding and acoustic signal classification. FREDERICK JELINEK, Ph.D., MIT, 1962. Speech recognition, statistical methods of natural language processing, information theory. Biomedical Engineering JOHN HEINZ, Sc.D., MIT, 1962. Speech communication, acoustics of speech and swallowing. MURRAY SACHS, Ph.D., MIT, 1966. Auditory neurophysiology and psychophysics. ERIC YOUNG, Ph.D., Johns Hopkins University, 1972. Auditory neurophysiology, neural modeling, sensory processes. Mathematical Sciences CAREY PRIEBE, Ph.D., George Mason University, 1993. Statistics, functional estimation, discriminant analysis, change point analysis, image analysis. COLIN WU, Ph.D., UC Berkeley, 1990. Statistics, semi-parametric models, robustness. Center Resources and Activities World-class computational resources Ample laboratory and office space for graduate students Weekly academic year seminar series Annual Speech Research Symposium Six-week international summer research workshop Affiliated Laboratories Center for Hearing Sciences, Johns Hopkins School of Medicine Communications Sciences Research Laboratory, Johns Hopkins Kennedy Krieger Institute Neural Encoding Laboratory, Johns Hopkins School of Medicine Sensory Communication Laboratory, Johns Hopkins Whiting School of Engineering Selected graduate courses offered in NLP and related areas: 600.403 - Learning and Modeling 600.404 - Artificial Neural Networks 600.435 - Artificial Intelligence 600.440 - Advanced Topics in Artificial Intelligence 600.465 - Introduction to Natural Language Processing 600.466 - Advanced Topics in Natural Language Processing 600.489 - Automated Reasoning 600.661 - Machine Learning 600.676 - Statistical Methods of Natural Language Analysis 520.435 - Digital Signal Processing 520.447 - Introduction to Information Theory and Coding 520.475 - Processing and Recognition of Speech 520.476 - Information Extraction from Speech and Text 520.639 - Information Theory 520.641 - Communication Theory 520.735 - Sensory Information Processing 050.405 - Cognitive AI II: Learning 050.603 - Lexical Processing 050.606 - Cognitive Neuropsychology of Language 050.624 - Topics in Syntactic Theory 050.625 - Linguistic Semantics 050.627 - Lexicon Seminar 050.642 - Computational Language Acquisition 050.643 - Laboratory in Computational Language Acquisition 050.801 - Research Seminar in Cognitive Neuropsychology 050.802 - Research Seminar in Cognitive Processes Center for Language and Speech Processing Johns Hopkins University 3400 N. Charles Street / Barton Hall Baltimore, MD 21218-2686 IMPORTANT: Indicate department(s) of interest Tel (410) 516-4237 Fax (410) 516-5050 Electronic Access e-mail: clsp@jhu.edu WWW: http://cspjhu.ece.jhu.edu Applications can also be requested via the web by filling out the on-line form available at http://cspjhu.ece.jhu.edu/admission.html The Johns Hopkins University does not discriminate on the basis of race, color, sex, religion, homosexuality, national or ethnic origin, age, disability or veteran status in any student program or activity administered by the University or with regard to admission or employment. Questions regarding Title VI, Title IX and Section 504 should be referred to Yvonne M. Theodore, Affirmative Action Officer, 205 Garland Hall (410-516- 8075). ------------------------------ Subject: workshop announcement From: David Heckerman Date: Fri, 20 Oct 95 16:41:08 TZ INVITATION TO ATTEND NIPS WORKSHOP ON LEARNING IN BAYESIAN NETWORKS AND OTHER GRAPHICAL MODELS Dec 1-2 1995 (Fri-Sat), Veil, Colorado A Bayesian network is a directed graphical representation of probabilistic relationships that people find easy to understand and use, often because the relationships have a causal interpretation. A network for a given domain defines a joint probability distribution for that domain, and algorithms exist for efficiently manipulating the joint distribution to determine probability distributions of interest. Over the last decade, the Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems. More recently, researchers have developed methods for learning Bayesian networks from data. These approaches will be the focus of this workshop. Issues to be discussed include (1) the opposing roles of prediction and explanation; (2) search, model selection, and capacity control; (3) representation issues, including extensions of the Bayesian network (e.g., chain graphs), the role of ``hidden'' or ``latent'' variables in learning, the modeling of temporal systems, and the assessment of priors; (4) optimization and approximation methods, including gradient-based methods, EM algorithms, stochastic sampling, and the mean field algorithms. In addition, we plan to discuss known relationships among Bayesian networks, Markov random fields, Boltzmann machines, loglinear models for contingency tables, Hidden Markov models, decision trees, and feedforward neural networks, as well as to uncover previously unknown ties. Detailed information about this workshop and the NIPS conference can be found at http://www.cs.cmu.edu/afs/cs/project/cnbc/nips/NIPS.html (conference) http://www.ai.mit.edu/people/jordan/workshop.html (workshop) Organizers: Wray Buntine Greg Cooper Dan Geiger Clark Glymour David Heckerman Geoffry Hinton Mike Jordan Steffen Lauritzen David Madigan Radford Neal Steve Omohundro Judea Pearl Stuart Russell Richard Scheines Peter Spirtes ------------------------------ Subject: Graduate Fellowships at UCI From: Michael Pazzani Date: Sat, 21 Oct 1995 09:18:21 -0700 The Information and Computer Science Department at UCI has received a GAANN grant from the Department of Education that can award 10 2-year fellowships to incoming students. The AI faculty at UCI are: Rina Dechter- Automated Reasoning, Constraint Networks, Bayesian Networks Rick Granger- Neural Networks, Computational Neuroscience Dennis Kibler- Machine Learning, Instance Based Learning, Prototype Learning Rick Lathrop- Intelligent Systems in Molecular Biology, Machine Learning Michael Pazzani- Machine Learning, Knowledge-intensive methods, Cognition Padhraic Smyth- Statistical Pattern Recognition, KDD. (Starting April 1996) The AI faculty have research funds from NSF, ARPA, ONR, and AFOSR as well as joint projects with industry to support graduate students as research assistants after the initial two year fellowships. Application material, including an online application, can be found on the WWW at http://www.ics.uci.edu/~gcounsel/applicantfaq.html or by sending e-mail to Email: theresa@ics.uci.edu Phone: (714) 824-2277 ------------------------------ Subject: Lectureship in Intelligent Systems available From: frisch@minster.cs.york.ac.uk Date: Fri, 20 Oct 1995 16:40:38 +0100 Lectureship in Computer Science (Intelligent Systems) University of York, UK Applications are invited for a permanent lectureship (equivalent to an Assistant Prof. in N. America) in the Department of Computer Science (which achieved the highest ratings in the 1992 Research Assessment Exercise and the 1994 Teaching Quality Assessment Exercise). The post will strengthen the Department's research and teaching activities in the area of Intelligent Systems, in which applicants must have a proven research record; experience of university teaching would be an additional advantage. The appointment is available from 1 March 1996, or as soon as possible thereafter, on the Lecturer Scale Grade A (GBP 14,317 to GBP 19,848 per annum). Informal enquiries may be made to Dr Keith Mander (Head of the Department of Computer Science, 01904-432727, mander@minster.york.ac.uk) or Dr Alan Frisch (Head of the Intelligent Systems Research Group, 01904-432745, frisch@minster.york.ac.uk). Six copies of applications with full curriculum vitae and the names of three referees should be sent by Monday, 13 November 1995 to the Personnel Officer, University of York, Heslington, York YO1 5DD. Further particulars are available from the Personnel Office (telephone 01904-432151). Applicants should quote the following reference number: X/3425. =========================================================================== Department of Computer Science University of York, UK Lectureship in Computer Science (Intelligent Systems) Information to Candidates Candidates should read this document in conjunction with general information about the Department of Computer Science (Facts and figures about the Department of Computer Science). The purpose of the present document is to provide additional information specific to the advertised post. 1. Lectureship in Intelligent Systems The Department of Computer Science has a strong research and teaching record. It was rated Grade 5 (i.e. attainable levels of international excellence in some sub- areas of activity and to attainable levels of national excellence in virtually all others) in the 1992 Research Assessment Exercise, and Excellent (i.e. demonstrably very high levels of achievement and best practice) in the 1994 Teaching Quality Assessment Exercise. In 1997 the Department will move into a spacious new building, accompanied by a significant expansion of its teaching and research activities. The Department has a small but growing research group in Intelligent Systems. The aim of the group is to investigate the theoretical principles of artificial intelligence and their application to real-world domains. Research topics covered by the group include: knowledge representation and reasoning, especially automated deduction; constraint logic programming and constraint solving; machine learning, especially case-based reasoning, learning to plan, learning grammars for natural languages; generalisation and computational learning theory; natural language processing. The Department expects the successful candidate to be able to make a significant contribution to this research group or a related area. A proven record in research is essential. Proven ability in university teaching will be an additional recommendation. 2. The post is offered on the Lecturer Scale Grade A (currently GBP 14,317 to GBP 19,848 per annum) with membership of USS. The candidate appointed may, immediately on starting his or her employment, join USS --the occupational pension scheme provided by this University-- under which the employee must contribute 6.35% of salary, the University contributing an amount equal to 18.55% of salary. Academic staff are required to retire on 30 September following the date on which they attain the age of 67. The University will meet the full cost within reason of removal of furniture and household effects within the United Kingdom for new members of staff. The extent of payment of removal expenses of staff coming from overseas is at the discretion of the Vice-Chancellor. Three estimates of removal costs (one of which should be from a York firm) must be obtained and the University will meet the full cost of the lowest estimate. 3. Informal inquiries may be made to the Head of Department, Dr Keith Mander (tel: 01904-432727, e-mail: mander@minster.york.ac.uk). Dr Alan Frisch (tel:01904-432745, email: frisch@minster.york.ac.uk), who leads the Intelligent Systems Group, is also available to answer queries. The address of the Department's World-Wide-Web page is http://www.cs.york.ac.uk/. Six copies of applications with full curriculum vitae and the names of three referees, should be sent to the Personnel Officer, University of York, Heslington, York YO1 5DD, by the date specified in the advertisement. Please quote the advertisement reference number: X/3425. ------------------------------ End of ML-LIST (Digest format) ****************************************