From Dave_Touretzky@DST.BOLTZ.CS.CMU.EDU Sun Jan 14 04:32:03 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Sun, 14 Jan 96 04:32:01 -0600; AA09653 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sun, 14 Jan 96 04:31:59 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa07147; 14 Jan 96 3:36:15 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa07145; 14 Jan 96 3:25:42 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa16211; 14 Jan 96 3:25:14 EST To: connectionists@cs.cmu.edu Reply-To: Dave_Touretzky@cs.cmu.edu Subject: postdoc position: computational neuroscience and rodent navigation Date: Sun, 14 Jan 96 03:14:52 EST Message-Id: <16098.821607292@DST.BOLTZ.CS.CMU.EDU> From: Dave_Touretzky@DST.BOLTZ.CS.CMU.EDU The Center for the Neural Basis of Cognition, a joint center of Carnegie Mellon University and the University of Pittsburgh, is accepting applications for postdoctoral positions in computational and cognitive neuroscience. One position for which candidates are actively being sought involves computational modeling and neurophysiological investigation of the rodent navigation system. Applicants should be either: * A neuroscientist with experience in single-unit recording from behaving animals, and some computer experience, who would like to do postdoctoral work involving computer modeling of the rodent hippocampal and head direction systems. * A computational neuroscientist already proficient in modeling biological neural networks, with a strong interest in helping to set up a neurophysiological recording facility as part of their postdoctoral training. Full details on the CNBC postdoctoral program are available on our web site at http://www.cs.cmu.edu/Web/Groups/CNBC -- follow the link to the NPC (Neural Processes in Cognition) program. Applications are due by February 1, but late applications may still be considered if the position is not filled. Persons interested in this position should contact me directly at dst@cs.cmu.edu. -- Dave Touretzky http://www.cs.cmu.edu/~dst dst@cs.cmu.edu Computer Science Department & Center for the Neural Basis of Cognition Carnegie Mellon University, Pittsburgh, PA 15213-3891 From ptodd@mpipf-muenchen.mpg.de Mon Jan 15 19:33:27 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 15 Jan 96 19:33:22 -0600; AA00331 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Mon, 15 Jan 96 19:33:19 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa09075; 15 Jan 96 18:22:24 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa09073; 15 Jan 96 17:57:43 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa17607; 15 Jan 96 17:57:05 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id ab13183; 15 Jan 96 9:51:46 EST Received: from hellbender.mpipf-muenchen.mpg.de by RI.CMU.EDU id aa20602; 15 Jan 96 7:47:28 EST Received: from localhost by hellbender.mpipf-muenchen.mpg.de; (5.65v3.2/1.1.8.2/25Oct95-1145AM) id AA00777; Mon, 15 Jan 1996 13:48:25 +0100 Message-Id: <9601151248.AA00777@hellbender.mpipf-muenchen.mpg.de> To: connectionists@cs.cmu.edu Subject: predoc/postdoc positions in Munich: modeling cognitive algorithms Date: Mon, 15 Jan 96 13:48:24 +0100 From: ptodd@mpipf-muenchen.mpg.de X-Mts: smtp (The following ad will appear in the APS Observer, and connectionists with interests in domain-specific forms of cognition are encouraged to apply. Feel free to write to me with questions about the group or how you or a continuing/graduating student might fit in. --Peter Todd) The Center for Adaptive Behavior and Cognition at the Max Planck Institute for Psychological Research in Munich, Germany is seeking applicants for 1 Predoctoral Fellowship (tax-free stipend DM 21,600) and 1 Postdoctoral Fellowship (tax-free stipend range DM 36,000-40,000) for one-year positions beginning in September 1996. Candidates should be interested in modeling satisficing decision-making algorithms in real-world environmental domains, and should have expertise in one of the following areas: computer simulation, biological categorization, evolutionary biology or psychology, experimental economics, judgment and decision making, risk perception. For a list of current researchers and interests, please send email to Dr. Peter Todd at ptodd@mpipf-muenchen.mpg.de . The working language of the center is English. Send applications (curriculum vitae, letters of recommendation, and reprints) by March 15, 1996 to Professor Gerd Gigerenzer, Center for Adaptive Behavior and Cognition, Max Planck Institute for Psychological Research, Leopoldstrasse 24, 80802 Munich, Germany. From datamine@aig.jpl.nasa.gov Tue Jan 16 07:45:30 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 16 Jan 96 07:45:24 -0600; AA00212 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 16 Jan 96 07:45:19 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa09101; 15 Jan 96 18:32:16 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa09078; 15 Jan 96 17:58:48 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa17613; 15 Jan 96 17:57:47 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa20414; 15 Jan 96 17:38:46 EST Received: from mathman.jpl.nasa.gov by CS.CMU.EDU id aa01078; 15 Jan 96 17:37:50 EST Received: by mathman.jpl.nasa.gov (4.1/JPL-AIG-1.0) id AA09697; Mon, 15 Jan 96 13:42:27 PST Date: Mon, 15 Jan 96 13:42:27 PST From: Data Mining Journal Message-Id: <9601152142.AA09697@mathman.jpl.nasa.gov> To: Connectionists@cs.cmu.edu Subject: New Journal -- Data Mining and Knowledge Discovery please post the following annoucement to your group, Thanks, Usama ________________________________________________________________ Usama Fayyad | Fayyad@aig.jpl.nasa.gov Machine Learning Systems Group | Jet Propulsion Lab M/S 525-3660 | (818) 306-6197 office California Institute of Technology | (818) 306-6912 FAX 4800 Oak Grove Drive | Pasadena, CA 91109 |http://www-aig.jpl.nasa.gov/ _____________________________________|__________________________ **************************************************************** 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. From geoff@salk.edu Tue Jan 16 23:24:27 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 16 Jan 96 23:24:23 -0600; AA10927 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 16 Jan 96 23:24:21 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab10918; 16 Jan 96 22:19:48 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab10916; 16 Jan 96 21:55:49 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19252; 16 Jan 96 21:54:59 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa04536; 16 Jan 96 13:17:49 EST Received: from [198.202.70.34] by CS.CMU.EDU id aa08116; 16 Jan 96 13:16:38 EST Received: from gauss.sdsc.edu (gauss.salk.edu) by salk.edu (4.1/SMI-4.1) id AA15105; Tue, 16 Jan 96 10:15:43 PST Date: Tue, 16 Jan 96 10:15:43 PST From: Geoff Goodhill Message-Id: <9601161815.AA15105@salk.edu> To: connectionists@cs.cmu.edu Subject: Preprint - revised information A few days ago I advertised a preprint entitled "Optimizing cortical mappings" by Goodhill, Finch and Sejnowski. Unfortunately since then the ftp and http details have changed. The new ones are ftp://ftp.cnl.salk.edu/pub/geoff/goodhill_nips96.ps.Z and http://www.cnl.salk.edu/~geoff Apologies, Geoff Goodhill From drl@eng.cam.ac.uk Tue Jan 16 23:24:28 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 16 Jan 96 23:24:22 -0600; AA10925 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 16 Jan 96 23:24:17 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa10918; 16 Jan 96 22:18:17 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10916; 16 Jan 96 21:55:47 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19247; 16 Jan 96 21:54:43 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa01300; 16 Jan 96 10:02:52 EST Received: from spanner.eng.cam.ac.uk by EDRC.CMU.EDU id aa25512; 16 Jan 96 10:02:06 EST Received: from dsl.eng.cam.ac.uk (via root@dsl.eng.cam.ac.uk [129.169.80.1]) by spanner.eng.cam.ac.uk with ESMTP id PAA10957; Tue, 16 Jan 1996 15:00:57 GMT Received: from dante.eng.cam.ac.uk (via drl@dante.eng.cam.ac.uk [129.169.80.16]) by dsl.eng.cam.ac.uk with SMTP id PAA11015; Tue, 16 Jan 1996 15:00:03 GMT From: drl@eng.cam.ac.uk Date: Tue, 16 Jan 96 15:00:00 GMT Message-Id: <9601161500.17843@dante.eng.cam.ac.uk> Received: by dante.eng.cam.ac.uk id AA17843 for svr-research@eng.cam.ac.uk; Tue, 16 Jan 96 15:00:00 GMT To: Connectionists@cs.cmu.edu, allstat@mailbase.ac.uk, svr-research@eng.cam.ac.uk Subject: Tech Report announcement Cc: kjd5@cam.eng.ac.uk The following technical report is available by anonymous ftp from the archive of the Speech, Vision and Robotics Group at the Cambridge University Engineering Department. Limits on the discrimination possible with discrete valued data, with application to medical risk prediction D. R. Lovell, C. R. Dance, M. Niranjan, R. W. Prager and K. J. Dalton Technical Report CUED/F-INFENG/TR243 Cambridge University Engineering Department Trumpington Street Cambridge CB2 1PZ England Abstract We describe an upper bound on the {\em accuracy} (in the ROC sense) attainable in two-alternative forced choice risk prediction, for a specific set of data represented by discrete features. By accuracy, we mean the probability that a risk prediction system will correctly rank a randomly chosen high risk case and a randomly chosen low risk case. We also present methods for estimating the maximum accuracy we can expect to attain using a given set of discrete features to represent data sampled from a given population. These techniques allow an experimenter to calculate the maximum performance that could be achieved, without having to resort to applying specific risk prediction methods. Furthermore, these techniques can be used to rank discrete features in order of their effect on maximum attainable accuracy. ************************ How to obtain a copy ************************ Via FTP: unix> ftp svr-ftp.eng.cam.ac.uk Name: anonymous Password: (type your email address) ftp> cd reports ftp> binary ftp> get lovell_tr243.ps.Z ftp> quit unix> uncompress NAME_tr243.ps.Z unix> lpr lovell_tr243.ps (or however you print PostScript) No hardcopies available. From bengioy@IRO.UMontreal.CA Wed Jan 17 16:15:08 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 17 Jan 96 16:15:05 -0600; AA29487 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 17 Jan 96 16:14:59 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa11072; 16 Jan 96 22:27:56 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10921; 16 Jan 96 21:57:21 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19261; 16 Jan 96 21:55:24 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa08983; 16 Jan 96 17:51:54 EST Received: from [132.204.32.54] by CS.CMU.EDU id aa11462; 16 Jan 96 17:50:59 EST Received: from oust.iro.umontreal.ca (oust.IRO.UMontreal.CA [132.204.34.42]) by saguenay.IRO.UMontreal.CA (8.6.12/8.6.12) with ESMTP id RAA25366; Tue, 16 Jan 1996 17:48:34 -0500 Received: (from bengioy@localhost) by oust.iro.umontreal.ca (8.6.12/8.6.12) id RAA01887; Tue, 16 Jan 1996 17:48:26 -0500 Message-Id: <199601162248.RAA01887@oust.iro.umontreal.ca> From: Yoshua Bengio Date: Tue, 16 Jan 1996 17:48:26 -0500 X-Mailer: Mail User's Shell (7.2.5 10/14/92) To: connectionists@cs.cmu.edu, neuron-request@CATTELL20.psych.upenn.edu Subject: Montreal workshop and spring school on NNs and learning algorithms Montreal Workshop and Spring School on Neural Nets and Learning Algorithms April 15-30 1996 Centre de Recherche Mathematique, Universite de Montreal MORE INFO AT: http://www.iro.umontreal.ca/labs/neuro/spring96/english.html This workshop and concentrated course on artificial neural networks and learning algorithms is organized by the Centre de Recherches Mathematiques of the University of Montreal (Montreal, Quebec, Canada). The first week of the the workshop will concentrate on learning theory, statistics, and generalization. The second week (and beginning of third) will concentrate on learning algorithms, architectures, applications and implementations. The organizers of the workshop are Bernard Goulard (Montreal), Yoshua Bengio (Montreal), Bertrand Giraud (CEA Saclay, France) and Renato De Mori (McGill). The invited speakers are G. Hinton (Toronto), V. Vapnik (AT&T), M. Jordan (MIT), H. Bourlard (Mons), T. Hastie (Stanford), R. Tibshirani (Toronto), F. Girosi (MIT), M. Mozer (Boulder), J.P. Nadal (ENS, Paris), Y. Le Cun (AT&T), M. Marchand (U of Ottawa), J. Shawe-Taylor (London), L. Bottou (Paris), F. Pineda (Baltimore), J. Moody (Oregon), S. Bengio (INRS Montreal), J. Cloutier (Montreal), S. Haykin (McMaster), M. Gori (Florence), J. Pollack (Brandeis), S. Becker (McMaster), Y. Bengio (Montreal), S. Nowlan (Motorola), P. Simard (AT&T), G. Dreyfus (ESPCI Paris), P. Dayan (MIT), N. Intrator (Tel Aviv), B. Giraud (France), B. Pearlmutter (Siemens), H.P. Graf (AT&T). TENTATIVE SCHEDULE (see details at http://www.iro.umontreal.ca/labs/neuro/spring96/english.html) Week 1 Introduction, learning theory and statistics April 15: Y. Bengio, J.P. Nadal, G. Dreyfus, B. Giraud April 16: Y. Bengio, F. Girosi, L. Bottou, J.P. Nadal, G. Dreyfus, B. Giraud April 17: V. Vapnik, L. Bottou, F. Girosi, M. Marchand, J. Shawe-Taylor, V. Vapnik April 18: J. Shawe-Taylor, V. Vapnik, R. Tibshirani, T. Hastie, M. Jordan April 19: M. Marchand, S. Bengio, R. Tibshirani, T. Hastie, M. Jordan Week 2 and 3 Algorithms, architectures and applications April 22: S. Haykin, H. Bourlard, M. Gori, M. Mozer, F. Pineda April 23: S. Haykin, F. Pineda, H. Bourlard, M. Mozer, J. Pollack, P. Dayan April 24: M. Gori, J. Pollack, P. Dayan, B. Pearlmutter, S. Becker, P. Simard April 25: S. Becker, G. Hinton, N. Intrator, B. Pearlmutter, S. Nowlan, Y. Le Cun April 26: S. Bengio, Y. Le Cun, S. Nowlan, N. Intrator, P. Simard April 29: J. Moody, Y. Bengio, J. Cloutier, H.P. Graf April 30: J. Moody, J. Cloutier, H.P. Graf REGISTRATION INFORMATION: $100 (Canadian) or 75 $US, if received before April 1st $150 (Canadian) or 115 $US, if received on or after April 1st $25 (Canadian) or 19 $US, for students and post-doctoral fellows. The number of participants will be limited, on a first-come first-served basis. Please register early! Have a look at http://www.iro.umontreal.ca/labs/neuro/spring96/english.html for more details, or directly load the registration form by ftp (postscript: ftp://ftp.iro.umontreal.ca/pub/neuro/registration.ps or ascii: ftp://ftp.iro.umontreal.ca/pub/neuro/registration.asc). Reduced hotel rates can be obtained by returning your registration form with your choice of hotel before March 15th. For more information, contact Louis Pelletier, pelletl@crm.umontreal.ca, 514-343-2197, fax 514-343-2254 Centre de Recherche Mathematique, Universite de Montreal, C.P. 6128, Succ. Centre-Ville, Montreal, Quebec, H3C-3J7, Canada. -- Yoshua Bengio Professeur Adjoint, Dept. Informatique et Recherche Operationnelle Pavillon Andre-Aisenstadt #3339 , Universite de Montreal, Dept. IRO, CP 6128, Succ. Centre-Ville, 2920 Chemin de la tour, Montreal, Quebec, Canada, H3C 3J7 E-mail: bengioy@iro.umontreal.ca Fax: (514) 343-5834 web: http://www.iro.umontreal.ca/htbin/userinfo/user?bengioy or http://www.iro.umontreal.ca/labs/neuro/ Tel: (514) 343-6804. Residence: (514) 738-6206 From heckerma@microsoft.com Wed Jan 17 16:15:11 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 17 Jan 96 16:15:08 -0600; AA29496 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 17 Jan 96 16:15:05 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa11307; 17 Jan 96 0:47:10 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa11303; 17 Jan 96 0:33:50 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19343; 17 Jan 96 0:33:07 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa12069; 16 Jan 96 22:36:57 EST Received: from tide10.microsoft.com by EDRC.CMU.EDU id aa28883; 16 Jan 96 22:36:23 EST Received: by tide10.microsoft.com; id TAA10183; Tue, 16 Jan 1996 19:51:48 -0800 Received: from unknown(157.54.17.74) by tide10.microsoft.com via smap (g3.0.3) id xma010141; Tue, 16 Jan 96 19:51:25 -0800 Received: from xnet1 (xnet1.microsoft.com [157.54.17.204]) by imail2.microsoft.com (8.7.1/8.7.1) with SMTP id TAA15890 for ; Tue, 16 Jan 1996 19:38:59 -0800 (PST) X-Received: from xmtp3 by xnet1 with receive; Tue, 16 Jan 1996 19:35:52 -0800 X-Received: from RED-70-MSG by XMTP3 with recvsmtp; Tue, 16 Jan 1996 19:36:11 -0800 Received: by red-70-msg.itg.microsoft.com with Microsoft Exchange (IMC 4.22.611) id <01BAE449.D524BC50@red-70-msg.itg.microsoft.com>; Tue, 16 Jan 1996 19:35:46 -0800 Message-Id: From: David Heckerman To: "Connectionists@CS.CMU.EDU" Subject: Summary: NIPS workshop on learning in graphical models Date: Tue, 16 Jan 1996 19:34:47 -0800 X-Mailer: Microsoft Exchange Server Internet Mail Connector Version 4.22.611 Encoding: 28 TEXT X-Msxmtid: XMTP3960117033611RECVSMTP[01.52.00]000000ad-3993 Summary: NIPS 95 Workshop on Learning in Bayesian Networks and Other Graphical Models We discussed the relationships between Bayesian networks, decomposable models, Markov random fields, Boltzmann machines, Hidden Markov models, stochastic grammars, and feedforward neural networks, exposing complementary strengths and weaknesses in the various formalisms. For example, Bayesian networks are particularly strong in their focus on explicit representations of probabilistic independencies (the arrows in a belief network have a strong semantics in this regard), their full use of Bayesian methods, and their focus on density estimation. Neural networks are particularly strong in their ties to approximation theory, and in their focus on predictive modeling in non-linear classification and regression contexts. Topics discussed included issues in optimization, including the use of gradient-based methods and EM algorithms; issues in approximation, including the use of mean field algorithms and stochastic sampling; issues in representation, including exploration of the roles of ``hidden'' or ``latent'' variables in learning; search methods for model selection and model averaging; and engineering issues. A more detailed summary, as well as pointers to slides and related papers can be found at http://www.research.microsoft.com/research/nips95bn/ From baluja@GS93.SP.CS.CMU.EDU Thu Jan 18 01:07:53 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 18 Jan 96 01:07:50 -0600; AA05849 Message-Id: <9601180707.AA04136@lucy.cs.wisc.edu> Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 18 Jan 96 01:07:48 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa11301; 17 Jan 96 0:39:57 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa11299; 17 Jan 96 0:33:00 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19337; 17 Jan 96 0:32:50 EST Received: from GS93.SP.CS.CMU.EDU by B.GP.CS.CMU.EDU id aa06246; 16 Jan 96 15:04:54 EST From: Shumeet Baluja Date: Tue, 16 Jan 96 15:04:11 EST To: connectionists@cs.cmu.edu Cc: baluja@cs.cmu.edu Subject: Paper: Medical Risk Evaluation - Rankprop and Multitask Learning Paper Available: -------------------------- Using the Future to "Sort Out" the Present: Rankprop and Multitask Learning for Medical Risk Evaluation Rich Caruana, Shumeet Baluja, and Tom Mitchell Abstract: -------------------------- A patient visits the doctor; the doctor reviews the patient's history, asks questions, makes basic measurements (blood pressure, ...), and prescribes tests or treatment. The prescribed course of action is based on an assessment of patient risk---patients at higher risk are given more and faster attention. It is also sequential---it is too expensive to immediately order all tests which might later be of value. This paper presents two methods that together improve the accuracy of backprop nets on a pneumonia risk assessment problem by 10-50\%. {\em Rankprop} improves on backpropagation with sum of squares error in ranking patients by risk. {\em Multitask learning} takes advantage of {\em future} lab tests available in the training set, but not available in practice when predictions must be made. Both methods are broadly applicable. Retrieval Information -------------------------- This paper will appear in NIPS 8. Available via the web from: http://www.cs.cmu.edu/~baluja/techreps.html From iconip96@cs.cuhk.hk Thu Jan 18 13:54:35 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 18 Jan 96 13:54:32 -0600; AA15647 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 18 Jan 96 13:54:30 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa12471; 17 Jan 96 18:15:40 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa12439; 17 Jan 96 17:51:40 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19967; 17 Jan 96 17:51:09 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa20685; 17 Jan 96 10:23:42 EST Received: from cucs18.cs.cuhk.hk by EDRC.CMU.EDU id aa01150; 17 Jan 96 10:23:15 EST Received: from bonsai.cs.cuhk.hk by cs.cuhk.hk with SMTP id VAA00980; Wed, 17 Jan 1996 21:32:02 +0800 From: iconip96 Message-Id: <199601171332.VAA00980@cs.cuhk.hk> Subject: *** ICONIP'96 FINAL CALL FOR PAPERS *** To: x=iconip96-program@cs.cuhk.hk Date: Wed, 17 Jan 1996 21:32:02 +0800 (HKT) X-Mailer: ELM [version 2.4 PL24] Mime-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Length: 9077 Please do not re-distribute this CFP to other lists. We apologize should you receive multiple copies of this CFP from different sources. ====================================================================== FINAL CALL FOR PAPERS 1996 INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING The Annual Conference of the Asian Pacific Neural Network Assembly ICONIP'96, September 24 - 27, 1996 Hong Kong Convention and Exhibition Center, Wan Chai, Hong Kong In cooperation with IEEE / NNC --IEEE Neural Networks Council INNS - International Neural Network Society ENNS - European Neural Network Society JNNS - Japanese Neural Network Society CNNC - China Neural Networks Council ====================================================================== The goal of ICONIP'96 is to provide a forum for researchers and engineers from academia and industry to meet and to exchange ideas on the latest developments in neural information processing. The conference also further serves to stimulate local and regional interests in neural information processing and its potential applications to industries indigenous to this region. The conference consists of two tracks. One is SCIENTIFIC TRACK for the latest results on Theories, Technologies, Methods, Architectures and Algorithms in neural information processing. The other is APPLICATION TRACK for various neural network applications in any engineering/technical field and any business/service sector. There will be a one-day tutorial on the neural networks for capital markets which reflects Hong Kong's local interests on financial services. In addition, there will be several invited lectures in the main conference. Hong Kong is one of the most dynamic cities in the world with world-class facilities, easy accessibility, exciting entertainment, and high levels of service and professionalism. Come to Hong Kong! Visit this Eastern Pearl in this historical period before Hong Kong's eminent return to China in 1997. Tutorials On Financial Engineering ================================== 1. Professor John Moody, Oregon Graduate Institute, USA "Time Series Modeling: Classical and Nonlinear Approaches" 2. Professor Halbert White, University California, San Diego, USA "Option Pricing In Modern Finance Theory And The Relevance Of Artificial Neural Networks" 3. The third tutorial speaker will also be an internationally well known expert in neural networks for the capital markets. Keynote Talks ============= 1. Professor Shun-ichi Amari, Tokyo University. "Information Geometry of Neural Networks" 2. Professor Yaser Abu-Mostafa, California Institute of Technology, USA "The Bin Model for Learning and Generalization" 3. Professor Leo Breiman, University California, Berkeley, USA "Democratizing Predictors" 4. Professor Christoph von der Malsburg, Ruhr-Universitat Bochum, Germany "Scene Analysis Based on Dynamic Links" (tentatively) 5. Professor Erkki Oja, Helsinki University of Technology, Finland "Blind Signal Separation by Neural Networks " *** PLUS AROUND 20 INVITED PAPERS GIVEN BY WELL KNOWN RESEARCHERS IN THE FIELD. *** CONFERENCE TOPICS ================= SCIENTIFIC TRACK: ----------------- * Theory * Algorithms & Architectures * Supervised Learning * Unsupervised Learning * Hardware Implementations * Hybrid Systems * Neurobiological Systems * Associative Memory * Visual & Speech Processing * Intelligent Control & Robotics * Cognitive Science & AI * Recurrent Net & Dynamics * Image Processing * Pattern Recognition * Computer Vision * Time Series Prediction * Optimization * Fuzzy Logic * Evolutionary Computing * Other Related Areas APPLICATION TRACK: ------------------ * Foreign Exchange * Equities & Commodities * Risk management * Options & Futures * Forecasting & Strategic Planning * Government and Services * Garments and Fashions * Telecommunications * Control & Modeling * Manufacturing * Chemical engineering * Transportation * Environmental engineering * Remote sensing * Power systems * Defense * Multimedia systems * Document Processing * Medical imaging * Biomedical application * Geophysical sciences * Other Applications CONFERENCE'S SCHEDULE ===================== Submission of paper February 1, 1996 Notification of acceptance May 1, 1996 Early registration deadline July 1, 1996 Tutorial on Financial Engineering Sept, 24, 1996 Conference Sept, 25-27, 1996 SUBMISSION INFORMATION ====================== Authors are invited to submit one camera-ready original and five copies of the manuscript written in English on A4-format (or letter) white paper with 25 mm (1 inch) margins on all four sides, in one column format, no more than six pages (four pages preferred) including figures and references, single- spaced, in Times-Roman or similar font of 10 points or larger, and printed on one side of the page only. Electronic or fax submission is not acceptable. Additional pages will be charged at USD $50 per page. Centered at the top of the first page should be the complete title, author(s), affiliation, mailing, and email addresses, followed by an abstract (no more than 150 words) and the text. Each submission should be accompanied by a cover letter indicating the contacting author, affiliation, mailing and email addresses, telephone and fax number, and preference of track, technical session(s), and format of presentation, either oral or poster. All submitted papers will be refereed by experts in the field based on quality, clarity, originality, and significance. Authors may also retrieve the ICONIP style, "iconip.tex" and "iconip.sty" files for the conference by anonymous FTP at ftp.cs.cuhk.hk in the directory /pub/iconip96. The address for information inquiries and paper submissions: ICONIP'96 Secretariat Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin, N.T., Hong Kong Fax (852) 2603-5024 E-mail: iconip96@cs.cuhk.hk http://www.cs.cuhk.hk/iconip96 ====================================================================== General Co-Chairs ================= Omar Wing, CUHK Shun-ichi Amari, Tokyo U. Advisory Committee ================== International ------------- Yaser Abu-Mostafa, Caltech Michael Arbib, U. Southern Cal. Leo Breiman, UC Berkeley Jack Cowan, U. Chicago Rolf Eckmiller, U. Bonn Jerome Friedman, Stanford U. Stephen Grossberg, Boston U. Robert Hecht-Nielsen, HNC Geoffrey Hinton, U. Toronto Anil Jain, Michigan State U. Teuvo Kohonen, Helsinki U. of Tech. Sun-Yuan Kung, Princeton U. Robert Marks, II, U. Washington Thomas Poggio, MIT Harold Szu, US Naval SWC John Taylor, King's College London David Touretzky, CMU C. v. d. Malsburg, Ruhr-U. Bochum David Willshaw, Edinburgh U. Lofti Zadeh, UC Berkeley Asia-Pacific Region ------------------- Marcelo H. Ang Jr, NUS, Singapore Sung-Yang Bang, POSTECH, Pohang Hsin-Chia Fu, NCTU., Hsinchu Toshio Fukuda, Nagoya U., Nagoya Kunihiko Fukushima, Osaka U., Osaka Zhenya He, Southeastern U., Nanjing Marwan Jabri, U. Sydney, Sydney Nikola Kasabov, U. Otago, Dunedin Yousou Wu, Tsinghua U., Beijing Organizing Committee ==================== L.W. Chan (Co-Chair), CUHK K.S. Leung (Co-Chair), CUHK D.Y. Yeung (Finance), HKUST C.K. Ng (Publication), CityUHK A. Wu (Publication), CityUHK B.T. Low (Publicity), CUHK M.W. Mak (Local Arr.), HKPU C.S. Tong (Local Arr.), HKBU T. Lee (Registration), CUHK K.P. Chan (Tutorial), HKU H.T. Tsui (Industry Liaison), CUHK I. King (Secretary), CUHK Program Committee ================= Co-Chairs --------- Lei Xu, CUHK Michael Jordan, MIT Erkki Oja, Helsinki U. of Tech. Mitsuo Kawato, ATR Members ------- Yoshua Bengio, U. Montreal Jim Bezdek, U. West Florida Chris Bishop, Aston U. Leon Bottou, Neuristique Gail Carpenter, Boston U. Laiwan Chan, CUHK Huishen Chi, Peking U. Peter Dayan, MIT Kenji Doya, ATR Scott Fahlman, CMU Francoise Fogelman, SLIGOS Lee Giles, NEC Research Inst. Michael Hasselmo, Harvard U. Kurt Hornik, Technical U. Wien Yu Hen Hu, U. Wisconsin - Madison Jeng-Neng Hwang, U. Washington Nathan Intrator, Tel-Aviv U. Larry Jackel, AT&T Bell Lab Adam Kowalczyk, Telecom Australia Soo-Young Lee, KAIST Todd Leen, Oregon Grad. Inst. Cheng-Yuan Liou, National Taiwan U. David MacKay, Cavendish Lab Eric Mjolsness, UC San Diego John Moody, Oregon Grad. Inst. Nelson Morgan, ICSI Steven Nowlan, Synaptics Michael Perrone, IBM Watson Lab Ting-Chuen Pong, HKUST Paul Refenes, London Business School David Sanchez, U. Miami Hava Siegelmann, Technion Ah Chung Tsoi, U. Queensland Benjamin Wah, U. Illinois Andreas Weigend, Colorado U. Ronald Williams, Northeastern U. John Wyatt, MIT Alan Yuille, Harvard U. Richard Zemel, CMU Jacek Zurada, U. Louisville From marni@salk.edu Thu Jan 18 13:54:39 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 18 Jan 96 13:54:37 -0600; AA15655 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 18 Jan 96 13:54:34 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ac12471; 17 Jan 96 18:18:57 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa12443; 17 Jan 96 17:52:35 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19978; 17 Jan 96 17:51:52 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa27255; 17 Jan 96 16:13:01 EST Received: from helmholtz.salk.edu by RI.CMU.EDU id aa03070; 17 Jan 96 16:12:26 EST Received: from chardin.salk.edu by salk.edu (4.1/SMI-4.1) id AA25701; Wed, 17 Jan 96 13:12:11 PST From: Marian Stewart Bartlett Received: (marni@localhost) by chardin.salk.edu (8.6.12/8.6.9) id NAA01871 for connectionists@cs.cmu.edu; Wed, 17 Jan 1996 13:12:10 -0800 Date: Wed, 17 Jan 1996 13:12:10 -0800 Message-Id: <199601172112.NAA01871@chardin.salk.edu> To: connectionists@cs.cmu.edu Subject: Preprint available The following preprints are available via anonymous ftp or http://www.cnl.salk.edu/~marni ------------------------------------------------------------------------ CLASSIFYING FACIAL ACTION Marian Stewart Bartlett, Paul A. Viola, Terrence J. Sejnowski, Beatrice A. Golomb, Jan Larsen, Joseph C. Hager, and Paul Ekman To appear in "Advances in Neural Information Processing Systems 8", D. Touretzky, M. Mozer, and M. Hasselmo (Eds.), MIT Press, Cambridge, MA, 1996. ABSTRACT The Facial Action Coding System, (FACS), devised by Ekman and Friesen, provides an objective means for measuring the facial muscle contractions involved in a facial expression. In this paper, we approach automated facial expression analysis by detecting and classifying facial actions. We generated a database of over 1100 image sequences of 24 subjects performing over 150 distinct facial actions or action combinations. We compare three different approaches to classifying the facial actions in these images: Holistic spatial analysis based on principal components of graylevel images; explicit measurement of local image features such as wrinkles; and template matching with motion flow fields. On a dataset containing six individual actions and 20 subjects, these methods had 89%, 57%, and 85% performances respectively for generalization to novel subjects. When combined, performance improved to 92%. nips95.ps.Z 7 pages; 352K compressed -------------------------------------------------------------------------- UNSUPERVISED LEARNING OF INVARIANT REPRESENTATIONS OF FACES THROUGH TEMPORAL ASSOCIATION Marian Stewart Bartlett and Terrence J. Sejnowski To appear in "The Neurobiology of Computation: Proceedings of the Annual Computational Neuroscience Meeting." J.M. Bower, ed. Kluwer Academic Publishers, Boston. ABSTRACT The appearance of an object or a face changes continuously as the observer moves through the environment or as a face changes expression or pose. Recognizing an object or a face despite these image changes is a challenging problem for computer vision systems, yet we perform the task quickly and easily. This simulation investigates the ability of an unsupervised learning mechanism to acquire representations that are tolerant to such changes in the image. The learning mechanism finds these representations by capturing temporal relationships between 2-D patterns. Previous models of temporal association learning have used idealized input representations. The input to this model consists of graylevel images of faces. A two-layer network learned face representations that incorporated changes of pose up to 30 degrees. A second network learned representations that were independent of facial expression. cns95.ta.ps.Z 6 pages; 428K compressed ========================================================================= FTP-host: ftp.cnl.salk.edu FTP-pathnames: /pub/marni/nips95.ps.Z and /pub/marni/cns95.ta.ps.Z URL: ftp://ftp.cnl.salk.edu/pub/marni WWW URL: http://www.cnl.salk.edu/~marni If you have difficulties, email marni@salk.edu From scott@cpl_mmag.nhrc.navy.mil Thu Jan 18 13:56:12 1996 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 18 Jan 96 13:54:34 -0600; AA15653 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 18 Jan 96 13:54:32 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab12471; 17 Jan 96 18:17:20 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab12439; 17 Jan 96 17:51:46 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19972; 17 Jan 96 17:51:29 EST Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa24414; 17 Jan 96 13:34:32 EST Received: from cpl_mma.nhrc.navy.mil by EDRC.CMU.EDU id aa02147; 17 Jan 96 13:33:26 EST Received: by cpl_mmag.nhrc.navy.mil (8.7.1/) id KAA13143 for delivery to Connectionists@CS.CMU.EDU; Wed, 17 Jan 1996 10:32:05 -0800 (PST) Date: Wed, 17 Jan 1996 10:32:05 -0800 (PST) From: Scott Makeig Message-Id: <199601171832.KAA13143@cpl_mmag.nhrc.navy.mil> To: Connectionists@cs.cmu.edu Subject: 2 papers applying neural networks to EEG data Announcing the availability of preprints of two articles to be published in the NIPS conference proceedings: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% INDEPENDENT COMPONENT ANALYSIS OF ELECTROENCEPHALOGRAPHIC DATA Scott Makeig Anthony J. Bell Naval Health Research Center Computational Neurobiology Lab P.O. Box 85122 The Salk Institute, P.O. Box 85800 San Diego CA 92186-5122 San Diego, CA 92186-5800 scott@cpl_mmag.nhrc.navy.mil tony@salk.edu Tzyy-Ping Jung Terrence J. Sejnowski Naval Health Research Center and Howard Hughes Medical Institute and Computational Neurobiology Lab Computational Neurobiology Lab jung@salk.edu terry@salk.edu ABSTRACT Because of the distance between the skull and brain and their different resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smearing of EEG data by volume conduction does not involve significant time delays, however, suggesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski(1994) is suitable for performing blind source separation on EEG data. The ICA algorithm separates the problem of source identification from that of source localization. First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show: (1) ICA training is insensitive to different random seeds. (2) ICA analysis may be used to segregate obvious artifactual EEG components (line and muscle noise, eye movements) from other sources. (3) ICA analysis is capable of isolating overlapping alpha and theta wave bursts to separate ICA channels (4) Nonstationarities in EEG and behavioral state can be tracked using ICA analysis via changes in the amount of residual correlation between ICA-filtered output channels. Sites: http://128.49.52.9/~scott/bib.html ftp://ftp.cnl.salk.edu/pub/jung/nips95b.ps.Z %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% USING NEURAL NETWORKS TO MONITOR ALERTNESS FROM CHANGES IN EEG CORRELATION AND COHERENCE Scott Makeig Tzyy-Ping Jung Naval Health Research Center Naval Health Research Center and P.O. Box 85122 Computational Neurobiology Lab San Diego, CA 92186-5122 The Salk Institute scott@cpl_mmag.nhrc.navy.mil jung@salk.edu Terrence J. Sejnowski Howard Hughes Medical Institute & Computational Neurobiology Lab The Salk Institute terry@salk.edu ABSTRACT We report here that changes in the normalized electroencephalographic (EEG) cross-spectrum can be used in conjunction with feedforward neural networks to monitor changes in alertness of operators continuously and in near-real time. Previously, we have shown that EEG spectral amplitudes covary with changes in alertness as indexed by changes in behavioral error rate on an auditory detection task (Makeig & Inlow, 1993). Here, we report for the first time that increases in the frequency of detection errors in this task are also accompanied by patterns of increased and decreased spectral coherence in several frequency bands and EEG channel pairs. Relationships between EEG coherence and performance vary between subjects, but within subjects, their topographic and spectral profiles appear stable from session to session. Changes in alertness also covary with changes in correlations among EEG waveforms recorded at different scalp sites, and neural networks can also estimate alertness from correlation changes in spontaneous and unobtrusively-recorded EEG signals. Sites: http://128.49.52.9/~scott/bib.html ftp://ftp.cnl.salk.edu/pub/jung/nips95a.ps.Z