From goldfarb@unb.ca Mon Jun 10 00:32:23 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id AAA00711 for ; Mon, 10 Jun 1996 00:31:36 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id AAA06227 for ; Mon, 10 Jun 1996 00:31:33 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa27471; 9 Jun 96 22:59:34 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa27469; 9 Jun 96 22:39:05 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa23692; 9 Jun 96 22:37:52 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa22594; 9 Jun 96 22:23:36 EDT Received: from hermes.csd.unb.ca by RI.CMU.EDU id aa00611; 9 Jun 96 22:22:58 EDT Received: (from goldfarb@jupiter.sun.csd.unb.ca [131.202.3.10]) by unb.ca (8.7.5/960430-08:40) id XAA12480; Sun, 9 Jun 1996 23:21:07 -0300 (ADT) Received: from localhost by jupiter.sun.csd.unb.ca (8.7.4/950215-16:05) id XAA22818; Sun, 9 Jun 1996 23:21:00 -0300 (ADT) Date: Sun, 9 Jun 1996 23:21:00 -0300 (ADT) From: Lev Goldfarb X-Sender: goldfarb@jupiter.sun.csd.unb.ca Reply-To: Lev Goldfarb To: inductive@unb.ca, ml@ics.uci.edu, colt@cs.uiuc.edu, connectionists@cs.cmu.edu, evolutionary-computing@mailbase.ac.uk, cogpsy@neuro.psy.soton.ac.uk, ai-stats@watstat.uwaterloo.ca, comp-neuro@smaug.bbb.caltech.edu, GA-List@aic.nrl.navy.mil, echos@dmi.ens.fr, kdd@gte.com, genetic@dcs.shef.ac.uk, comp-speech@cs.utexas.edu, tanns@cs.unc.edu, intcon@phoenix.ee.unsw.edu.au, alife@cognet.ucla.edu, spp@umiacs.umd.edu, Vision-List@TELEOS.COM, EP-LIST@magenta.me.fau.edu, cogni-info@univ-lyon1.fr, neuropl@plearn.edu.pl, philos-l@liverpool.ac.uk, neur-sci@dl.ac.uk, enns-list@dcs.kcl.ac.uk, DAI-List@ece.sc.edu, dbworld@cs.wisc.edu, TIERRA@life.slhs.udel.edu, cells@tce.ing.uniroma1.it, reinforce@cs.uwa.edu.au Subject: Call for papers for a Special Issue of Pattern Recognition: What is inductive learning? Message-ID: MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII My apologies if you receive multiple copies of this message. Please, post it. ************************************************************************ Call for papers ----------------- Special Issue of Pattern Recognition (The Journal of the Pattern Recognition Society) WHAT IS INDUCTIVE LEARNING: ON THE FOUNDATIONS OF PATTERN RECOGNITION, AI, AND COGNITIVE SCIENCE Guest editor: Lev Goldfarb Faculty of Computer Science University of New Brunswick Fredericton, N.B., Canada The "shape" of AI (and, partly, of cognitive science), as it stands now, has been molded largely by the three founding schools (at Massachusetts institute of Technology, Carnegie-Mellon and Stanford Universities). This "shape" stands now fragmented into several ill-defined research agendas with no clear basic SCIENTIFIC problems (in the classical understanding of the term) as their focus. It appears that four factors have contributed to this situation: inability to focus on the central cognitive process(es), lack of understanding of the structure of advanced scientific models, failure to see the distinction between the computational/logical and mathematical models, and the relative abundance of research funds for AI during the last 35 years. The resulting research agendas have prevented AI from cooperatively evolving into a scientific discipline with some central "real" problems that are inspired by the basic cognitive/biological processes. The candidates for such basic processes could come only from the central/common perceptual processes and only much later employed by the "higher", e.g. language, processes: the period during which the "higher" level processes have evolved is insignificant compared to that in which the development of the perceptual processes took place (compare also the anatomical development of the brain which does not show any basic changes with the development of the "higher" processes). Moreover, the partisan tradition in the development of AI may have also inspired the recent "connectionist revolution" as well as other smaller "revolutions", e.g., that related to the "genetic" learning. As a result, in particular, even the most "reputable" connectionist histories of the field of pattern recognition, which was formed more than three decades ago and to which the connectionism properly belongs, show amazing ignorance of the major developments in the parent (pattern recognition) field: the emergence of two important and formally quite irreconcilable recognition paradigms--vector space and syntactic. The latter ignorance is even more instructive in view of the fact that many engineers who got involved with the field of pattern recognition through the connectionist "movement" are also ignorant of the above two paradigms that were discovered and developed within largely applied/engineering parent field of pattern recognition. As far as the inception of a scientific field is concerned, it should be quite clear that the initial choice of the basic scientific problem(s) is of decisive importance. This is particularly true for cognitive modeling where the path from the model to the experiment and the reverse path are much more complex than was the case, for example, at the inception of physics. In this connection, a very important question arises, which will be addressed in the special issue: What form will the future/adequate cognitive models take? Furthermore, may be, as many cognitive scientists argue, since we are in a prescientific stage, we should simply continue to collect more and more data and not worry about the future models. The answer to the last argument is quite clear to me: look very carefully at the "data" and the corresponding experiments and you will note that no data can be even collected without an underlying model, which always includes both formal and informal components. In other words, we cannot avoid models (especially in cognitive science, where the path from the model to the experiment will be much longer and more complex than is the case in all other sciences). Therefore, paraphrasing Friedrich Engels's thought on the role of philosophy in science, one can say that there is absolutely no way to do a scientific experiment without the underlying model and the difference between a good scientist and a bad one has to do with the degree to which each realizes this dependence and actively participates in the selection of the corresponding model. It goes without saying that, at the inception of the science, the decision on which cognitive process one must focus initially should precede the selection of the model for the process. As to the choice of the basic scientific problem, or basic cognitive process, it appears that the really central cognitive process is that of inductive learning, which might have been marked so by many great philosophers of the past four centuries (e.g., Bacon, Descartes, Pascal, Locke, Hume, Kant, Mill, Russell, Quine) and even earlier (e.g., Aristotle). The insistence of such outstanding physiologists and neurophysiologists as Helmholtz and Barlow on the central role of inductive learning processes is also well known. However, in view of the difficulties associated with developing an adequate inductive learning model, researchers in AI and to a somewhat lesser extent in cognitive science have decided to view inductive learning not as a central process at all, i.e., they decided to "dissolve" the problem. It became clear to me that the above difficulties are related to the development of a genuinely new (symbolic) mathematical framework that can SATISFACTORILY define the concept of INDUCTIVE CLASS REPRESENTATION (ICR), i.e., the nature of encoding essentially infinite data set on the basis of a small finite set. (The most known as well as critical to the development of mathematics example of ICR is that of the classical Peano representation of the set of natural numbers--one element plus one operation--used in mathematical induction.) Thus, the main differences between inductive learning models should be viewed in light of the differences between the formal means, i.e. mathematical structures, offered by various models for representing the class inductively. I will also argue (in one of the papers) that the classical mathematical (numeric) models, including the vector space and probabilistic models, offer inadequate axiomatic frameworks for capturing the concept of ICR. As Peter Gardenfors aptly remarked in his 1990 paper, "induction has been called 'the scandal of philosophy' [and] unless more consideration is given to the question of which form of knowledge representation is appropriate for mechanized inductive inferences, I'm afraid that induction may become a scandal of AI as well." I strongly believe that all attempts to "dissolve" the inductive learning processes are futile and, moreover, that these processes are central cognitive processes for all levels of processing, hence the earlier workshop in Toronto (May 20-21) under the same title and the present Special Issue. I invite all researchers seriously interested in the scientific foundations of cognitive science, AI, or pattern recognition to submit the papers addressing, in addition to other relevant issues, the following questions: * What is the role of mathematics in cognitive science, AI, and pattern recognition? * Are there any central cognitive processes? * What is inductive learning? * What is inductive class representation (ICR)? * Are there several basic inductive learning processes? * Are the inductive learning processes central? * What are the relations between inductive learning processes and the known physical processes? * What is the relationship between the measurement processes and inductive learning processes (e.g., retina as a structured measurement device)? * What is the role of inductive learning in sensation and perception (vision, hearing, etc.)? * What is the relation between the inductive learning, categorization, and pattern recognition? * What is the relation between the supervised/inductive learning and the unsupervised learning? * What is the role of inductive learning processes in language acquisition? * What are the relationships, if any, between the inductive class representation (ICR) and the basic object representation (from the class)? * What are the differences between the mathematical structures employed by the known inductive learning models for capturing the corresponding ICRs? * What is the role of inductive learning in memory and knowledge representation? * What are the relations, if any, between the ICR and mental models and frames? When preparing the manuscript, please conform to the standard submission requirements given in journal Pattern Recognition, which could be faxed or mailed if necessary. Hardcopies (4) of each submission should be mailed to Lev Goldfarb Faculty of Computer Science University of New Brunswick P.O. Box 4400 E-mail: goldfarb@unb.ca Fredericton, N.B. E3B 5A3 Tel: 506-453-4566 Canada Fax: 506-453-3566 by the SUBMISSION DEADLINE, August 20, 1996. The review process should take about 4-5 weeks and will take into account the relevance, quality, and originality of the contribution. Potential contributors are encouraged to contact me with any questions they might have. ************************************************************************** -- Lev Goldfarb http://wwwos2.cs.unb.ca/profs/goldfarb/goldfarb.html From john@dcs.rhbnc.ac.uk Mon Jun 10 17:27:01 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id RAA15520 for ; Mon, 10 Jun 1996 17:26:56 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id RAA16325 for ; Mon, 10 Jun 1996 17:26:54 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa28370; 10 Jun 96 16:44:30 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa28368; 10 Jun 96 16:30:16 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa24257; 10 Jun 96 16:29:51 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id ah29599; 10 Jun 96 11:55:56 EDT Received: from dns1.rhbnc.ac.uk by RI.CMU.EDU id aa02650; 10 Jun 96 10:40:06 EDT Received: from localhost (localhost [127.0.0.1]) by platon.cs.rhbnc.ac.uk (8.6.9/8.6.9) with SMTP id PAA05889 ; Mon, 10 Jun 1996 15:42:05 +0100 From: John Shawe-Taylor Message-Id: <199606101442.PAA05889@platon.cs.rhbnc.ac.uk> X-Authentication-Warning: platon.cs.rhbnc.ac.uk: Host localhost didn't use HELO protocol To: colt@cs.uiuc.edu, Connectionists@cs.cmu.edu Subject: RESEARCH ASSISTANT POST Date: Mon, 10 Jun 96 15:42:05 +0100 X-Mts: smtp --------------------------------------------------------------- RESEARCH ASSISTANT POST Royal Holloway/London School of Economics, University of London --------------------------------------------------------------- Jonathan Baxter has been working at Royal Holloway and London School of Economics on an EPSRC (a UK funding council) funded project entitled `The Canonical Metric in Machine Learning'. Attached below is an abstract of the project which has a further year to run. Jonathan is resigning from the project to move to the Australian National University, where he will continue to work along similar lines. EPSRC have given us permission to recruit a replacement to start any time between 5th July and 5th January 97 and to run for a further 12 months provided that they are suitable for the work involved. If you know anyone who would be interested, it would be very helpful if they could visit London before Jonathan leaves on 5th July. Jonathan, Martin and I will be attending COLT and so could discuss the project in detail with anyone interested at that time. The rate of pay is 19600 pounds pa paid half through each institution. The slant taken could be towards more implementational work or alternatively (and perhaps preferably in view of the funding committee being mathematical) more theoretical. Anyone with an interest should not hesitate to contact us for more information. Best wishes John Shawe-Taylor, Martin Anthony and Jonathan Baxter ---------- Canonical Metric in Machine Learning (Abstract and progress) The performance of a learning algorithm is fundamentally limited by the features it uses. Thus discovering sets of good features is of major importance in machine learning research. The principle aim of this project is to further our theoretical knowledge of the feature discovery process, and also to implement practical solutions for feature discovery in problems such as character recognition and speech recognition. The theoretical aspect of the project builds on work by the previous research assistant (Jonathan Baxter) showing that if a learner is embedded within and environment of related tasks then the learner can learn features that are appropriate for learning all tasks in the environment. This process can be viewed as "Learning to Learn". One can also show that the environment of learning problems induces a natural metric (the "canonical metric") on the input space of the learner. Knowledge of this metric enables the learner to perform optimal quantization of the input space, and hence to learn optimally within the environment. The main theoretical focus of the project is to further investigate the theory of the canonical metric and its relation to learning. We are currently applying these theoretical ideas to the problem of Japanese character recognition and so far we have achieved notable success. The practical part of the project will be to continue these investigations and to also investigate applications to speech recognition. Further information on the background material to this project may be found in Neurocolt technical reports: 95-45 95-46 and 95-47. Also see Jonathan Baxter's talk at this year's COLT. From pazzani@super-pan.ICS.UCI.EDU Tue Jun 11 01:12:51 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id BAA22790 for ; Tue, 11 Jun 1996 01:12:38 -0500 Received: from paris.ics.uci.edu (paris.ics.uci.edu [128.195.1.50]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id BAA20756; Tue, 11 Jun 1996 01:12:34 -0500 Received: from super-pan.ics.uci.edu by paris.ics.uci.edu id aa01898; 10 Jun 96 21:25 PDT To: ML-LIST:; Subject: Machine Learning List: Vol. 8, No. 11 Reply-to: ml@ics.uci.edu Date: Mon, 10 Jun 1996 21:05:27 -0700 From: Michael Pazzani Message-ID: <9606102125.aa01898@paris.ics.uci.edu> Machine Learning List: Vol. 8, No. 11 Monday, June 10, 1996 Contents: Submissions to ML-LIST Interestingness in KDD result echoes Lenat 20 years ago... Deadline Extension: special issue of JLP on ILP CfP ISFL'97: 2nd Int Symp on Fuzzy Logic, Feb 12-14,97, Zurich Test drive different algorithms on your problem Machine learning tools : SIPINA_W Version 1.3 CfP ISFL'97: 2nd Int Symp on Fuzzy Logic, Feb 12-14,97, Zurich Special Issue of Pattern Recognition: What is inductive learning? Applications of Neural Networks to Signal Processing CfP ISFL'97: 2nd Int Symp on Fuzzy Logic, Feb 12-14,97, Zurich 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 obtained from http://www.ics.uci.edu/AI/ML/Machine-Learning.html ---------------------------------------------------------------------- Date: Sun, 09 Jun 1996 15:01:38 -0700 Subject: Submissions to ML-LIST From: Michael Pazzani I've received a few inquiries recently about ML-LIST policies, and I thought I'd try to clarify things. ML-LIST is intended for discussion of topics related to machine learning, and announcements of conferences, journals, books related to this topic. The following types of articles are usually not included: * Announcements of conferences, journals, etc. related to other subfields of AI (e.g., Natural Language Processing) or General AI unless there is a explicit mention of learning issues in the conference announcement. * Beginning type questions (e.g., "Where can I find the code for ID3?"). * Announcements of single papers. There are over 1500 subscribers to ML-LIST and if each person announced each paper, ML-LIST would be too long. Occasionally, one slips through, or I include one if it is relevant to a recent discussion on ML-LIST. On the other hand, an announcement of all the papers from a particular site are welcome. * Second announcements for call for papers, or conference registration. Very brief reminders are included which include a deadline and URL for more information. * Postscript, LaTex, MSWORD, RTF, etc. are not included. Submissions should be in pure ASCII. Finally to make my life easier: * Please use a descriptive subject. (Not SUBMISSION FOR ML-LIST) * Please mail to ml@ics.uci.edu * Please don't include a long signature and a cute quote * Please don't use a series of "---" to separate sections of an article or emulate underlining or as a line in a registration form. ML-LIST (and all digests) use this to separate articles. ------------------------------ Date: Mon, 3 Jun 1996 10:17:49 -0400 Subject: Interestingness in KDD result echoes Lenat 20 years ago... From: Larry Hunter I find the discussion of Katharina Morik and Gregory Piatetsky-Shapiro (about KDD discovered rules that are very strong or close to exact not being interesting) reminiscent of one of Doug Lenat's original "interestingness" heuristics from AM, 20 years ago. Lenat's heuristic stated that concepts with too few examples or too many examples weren't interesting. That heuristic was crucial to AM's "discovery" of prime numbers. I wonder if other AM "interestingness" heuristics might also be useful in KDD? [See, e.g., D. Lenat "Automated Theory Formation in Mathematics," 5th IJCAI, 1977, p. 833] Larry Hunter ------------------------------ Date: Mon, 3 Jun 96 18:00:39 BST Subject: Deadline Extension: special issue of JLP on ILP From: David Page DEADLINE EXTENSION for submission to The Journal of Logic Programming, special issue on Inductive Logic Programming: the submission deadline has been extended from July 1 to September 1, 1996. Please email the title and a brief abstract of your intended submission on or before August 1, 1996 to dpage@comlab.ox.ac.uk ********************************************************** CALL FOR PAPERS ********************************************************** Special Issue: INDUCTIVE LOGIC PROGRAMMING THE JOURNAL OF LOGIC PROGRAMMING Editor-in-Chief: M. Bruynooghe Founding Editor: J.A. Robinson Guest Editors: Stephen Muggleton and David Page Oxford University Computing Laboratory Inductive Logic Programming (ILP) is a growing research area spawned by Logic Programming and Machine Learning. 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: SEPT 1, 1996 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 five (5) copies of submissions to: David Page Phone: +44-1865-283-520 Oxford University Computing Lab Fax: +44-1865-273-839 Wolfson Building Email: dpage@comlab.ox.ac.uk Parks Road Oxford, OX1 3QD United Kingdom JLP LaTeX style files will be made available and authors are en- couraged to use them to speed up the production process. Authors are REQUIRED to email a title and a 4-5 line abstract to arrive by August 1, 1996 to dpage@comlab.ox.ac.uk to facilitate the as- signment of reviewers in advance. NORTH-HOLLAND ------------------------------ Date: Fri, 7 Jun 1996 15:29:12 -0600 (MDT) Subject: CfP ISFL'97: 2nd Int Symp on Fuzzy Logic, Feb 12-14,97, Zurich From: icsc@freenet.edmonton.ab.ca Upon multiple request the final deadline for ISFL'97 submissions has been extended to July 10, 1996. ISFL'97 Second International ICSC Symposium on FUZZY LOGIC AND APPLICATIONS Information: For further information, please contact either of the following: - ICSC Canada, P.O. Box 279, Millet, AB T0C 1Z0, Canada E-mail: icsc@freenet.edmonton.ab.ca Fax: +1-403-387-4329 Phone: +1-403-387-3546 - ICSC Switzerland, P.O. Box 657, CH-8055 Zurich, Switzerland Fax: +41-1-761-9627 - Prof. Nigel Steele, Chairman ISFL'97, Coventry University, U.K. E-mail: nsteele@coventry.ac.uk Fax: +44-1203-838585 Phone: +44-1203-838568 ------------------------------ Date: Thu, 30 May 1996 21:37:09 -0700 Subject: Test drive different algorithms on your problem From: Ronny Kohavi Recently we have seen many claims (sometimes seemingly contradictory) such as: 1. Very simple classification rules perform well on most commonly used datasets (Holte, 1993). 2. There is no free lunch. No algorithm can perform no better than any other on average if all targets are equiprobable (Wolpert, 1994). 3. "It cannot be emphasized enough that no claim whatsoever is being made in this paper that all algorithms are equivalent *in practice*, in the real world. In particular, no claim is being made that one should not use cross-validation in the real world" (Wolpert, 1994). 4. Generalization is a zero-sum enterprise; for every performance gain in some subclass of learning situations there is an equal and opposite effect in others (Schaffer, 1994). 5. "Rules are More than Trees" (Parsaye, slide title in the VLDB summit,1996). 6. In your paper, you should have compared with C4.5rules and not C4.5, since it is known to be *much* better (naive reviewer). 7. "[Converting trees to rules] leads to a production rule classifier that is usually about as accurate as a pruned tree..." (Quinlan, 1993). 8. Decision-trees induction can easily be made asymptotically Bayes optimal (Gordon and Olshen, 1978, 1984). (BTW, no such claim has ever been made for decision rule induction.) 9. Nearest neighbors are Bayes optimal. Asymptotically no algorithm can do better (Fix and Hodges, 1951). 10.Neural network and statistical methods do better in some areas and Machine Learning procedures in others (Brazdil and Henery in the Statlog book, p. 175). We have recently done an experiment comparing 17 algorithms on 8 large datasets at the UC Irvine repository. The comparison is similar to the StatLog comparison with two major differences: all datasets are at UCI, and all algorithms can be trivially run from MLC++ by setting a few environment variables. The following paper includes a description of the claims made above and what we believe is the right way to proceed if you are only interested in accuracy: try a few algorithms on your specific problem. (In practice, of course, comprehensibility in the KDD cycle may be much more important than initial accuracy.) The paper is geared towards end-users not necessarily familiar with machine learning. Some theoretical references are left to the appendix. Our results show that while there are no clear winners throughout (as expected), 1R and T2 (very simple classifiers) were very poor performers in general. A description of MLC++ and the actual comparison (and how to repeat them) is provided in the following paper. Data Mining using MLC++ A Machine Learning Library in C++ Ron Kohavi Dan Sommerfield James Dougherty Data Mining and Visualization Platform Group Silicon Graphics, Inc. Sun Microsystems {ronnyk,sommda}@engr.sgi.com jamesd@eng.sun.com The paper is available at ftp://starry.stanford.edu/pub/ronnyk/mlc96.ps.Z or under publications off http://robotics.stanford.edu/~ronnyk -- Ronny Kohavi (ronnyk@sgi.com, http://robotics.stanford.edu/~ronnyk) ------------------------------ Date: Tue, 4 Jun 1996 11:59:47 +0200 (MET DST) Subject: Machine learning tools : SIPINA_W Version 1.3 From: "abdelkader.zighed" ~~~~~~~~~~~~~SIPINA v1.3~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DESCRIPTION ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This version contains several methods of induction graphs and some tools to evaluate the rules bases. We summarise the main modules here : a - Import / Manipulation of data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The SIPINA_W files (.DAT) have the ASCII format but you may import data directly from databases with a dBase format (.DBF) or a Paradox format (.DB), or you may export data from a Lotus format spreadsheet (.WKS). Continuous data may be recoded by different contextual or non-contextual discretisation methods: * Chi-Merge [Kerber 1992], * MDLPC [Fayadd & Irani 1992], * FUSINTER [ZIGHED 1995], * FUSBIN[ZIGHED,1996] ~~~~~~~~~~~~~~~~~~ (New) b - Methods ~~~~~~~ Several methods are implemented: * CART [Breiman & al. 1984], complete program proposing two criteria (Twoing Rule, Gini index), as well as the pruning algorithm; * Elisee [Bouroche & Tenenhaus 1970], binary segmentation method using the Chi-2 criterion; * ID3 [Quinlan 1979/1986]; * C 4.5 [Quinlan 1992], includes the pruning and the simplification of rules; * Chi2-link: a method using the Chi-2 critical probability as selection criterion, cf.Mingers 1987; * SIPINA [ZIGHED 1985/1992]: generalisation of trees by induction graphs, including dynamically the discretisation methods seen above. c - Tests and Evaluation ~~~~~~~~~~~~~~~~~~~~~ You may divide the data file into a learning sample and a test sample, and then you execute the data processing on the first one, generate the rules, followed by the validation on the second sample. But you may also activate a cross-validation where the draw of a sub-sample may be either randomly or stratified. You can also use a bootstrap procedure. ~~~~~~~~~~~~~~~~~~ (new) The consequent rules on each analysis are saved in different bases. Rules can be saved in pruction rules format or Prolog. ~~~~~ (new) d - Automatic / Interactive Learning ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When using the automatic learning procedure you only have to choose the method and execute the analysis. The interactive learning mode enables you to force the operations to be executed (Split, Merge), as well as the variables used (surrogate split) on each vertex. The vertex inspection makes it possible to visualise the available information on the selected vertex: distribution of the classes, observations list, distribution function on each variable, variables=92 power on competing splits. e - Advanced manipulations of rules ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ e.1. Extracting rules The generation of rules consequent to the graph has been improved. From each non-initial vertex it is now possible to produce prediction rules which can be evaluated through : * their error rate, * their corresponding number of observations, * an implication test based upon the Lerman statistic [Lerman,1981] * and the gap-test based upon the Chi-square statistic. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(new) e.2. Rules bases manipulation The rules bases may evolve by the fusion of two or more bases; the user has the possibility to input rules manually and to evaluate them by means of the data set. e.3. Selection of the best rules by validation During the application step of a rules base on a test or generalisation sample the specification of the selection criterion for the competing rules may be altered (an individual may respond to two rules, both having different conclusions; this is mostly possible when executing a merge of rules bases). The criteria are: * minimisation of the error rate, * maximisation of a rule=92s number of individuals, * maximisation of the Goodman index [1988], * maximisation of the intensity of implication, * new strategies such as bagging [Breiman 1996]. ~~~~~~~~~~~~~~~~~~~~~~ (new) e.4. Optimisation and Simplification. The consequent rules of an induction graph may be optimised and simplified. The applicable methods are: * detection and elimination of recurring premises, * use of a symbolic algorithm exploring the whole description domain, * algorithm of Quinlan [1987]: a hill-climbing for search the minimum, pessimistic error rate. f - Technical Limitations ~~~~~~~~~~~~~~~~~~~~~ The theoretical capacities of the software are: * 16.384 attributes * 2^32 - 1 cases actually, the limitations are those of the computer. g- Status ~~~~~~ Shareware. h - How you can get SIPINA-W v1.3 ? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You can obtain SIPINA by ftp anonymous from : eric.univ-lyon2.fr /pub/sipina i - Installation ~~~~~~~~~~~~ to install this version, You download LESIPINA.EXE. LESIPINA.EXE is a self-extracting file. Copy it in a temporary directory, and execute. The installation file is SETUP.EXE. Please, Click on OK when the soft ask you another disk. j - Updated by ~~~~~~~~~~ Ricco Rakotomalala on 1996-06-March (rakotoma@univ-lyon2.fr) k - Contact ~~~~~~~ Prof. D.A. Zighed, Organisation: University of Lyon2, e-mail : zighed@univ-lyon2.fr, Tel.: (33) 78 77 23 76, Fax.: (33) 78 77 23 75, Adress : E.R.I.C._Lyon bat.L 5 av. Pierre Mendes-France 69676 Bron Cedex France. WEB : http://eric.univ-lyon2.fr/eric.html ------------------------------ Date: Fri, 7 Jun 1996 15:29:12 -0600 (MDT) Subject: CfP ISFL'97: 2nd Int Symp on Fuzzy Logic, Feb 12-14,97, Zurich From: icsc@freenet.edmonton.ab.ca Upon multiple request the final deadline for ISFL'97 submissions has been extended to July 10, 1996. ISFL'97 Second International ICSC Symposium on FUZZY LOGIC AND APPLICATIONS Information: For further information, please contact either of the following: - ICSC Canada, P.O. Box 279, Millet, AB T0C 1Z0, Canada E-mail: icsc@freenet.edmonton.ab.ca Fax: +1-403-387-4329 Phone: +1-403-387-3546 - ICSC Switzerland, P.O. Box 657, CH-8055 Zurich, Switzerland Fax: +41-1-761-9627 - Prof. Nigel Steele, Chairman ISFL'97, Coventry University, U.K. E-mail: nsteele@coventry.ac.uk Fax: +44-1203-838585 Phone: +44-1203-838568 ------------------------------ Date: Sun, 9 Jun 1996 23:21:00 -0300 (ADT) Subject: Special Issue of Pattern Recognition: What is inductive learning? From: Lev Goldfarb Special Issue of Pattern Recognition (The Journal of the Pattern Recognition Society) WHAT IS INDUCTIVE LEARNING: ON THE FOUNDATIONS OF PATTERN RECOGNITION, AI, AND COGNITIVE SCIENCE Guest editor: Lev Goldfarb Faculty of Computer Science University of New Brunswick Fredericton, N.B., Canada The "shape" of AI (and, partly, of cognitive science), as it stands now, has been molded largely by the three founding schools (at Massachusetts institute of Technology, Carnegie-Mellon and Stanford Universities). This "shape" stands now fragmented into several ill-defined research agendas with no clear basic SCIENTIFIC problems (in the classical understanding of the term) as their focus. It appears that four factors have contributed to this situation: inability to focus on the central cognitive process(es), lack of understanding of the structure of advanced scientific models, failure to see the distinction between the computational/logical and mathematical models, and the relative abundance of research funds for AI during the last 35 years. The resulting research agendas have prevented AI from cooperatively evolving into a scientific discipline with some central "real" problems that are inspired by the basic cognitive/biological processes. The candidates for such basic processes could come only from the central/common perceptual processes and only much later employed by the "higher", e.g. language, processes: the period during which the "higher" level processes have evolved is insignificant compared to that in which the development of the perceptual processes took place (compare also the anatomical development of the brain which does not show any basic changes with the development of the "higher" processes). Moreover, the partisan tradition in the development of AI may have also inspired the recent "connectionist revolution" as well as other smaller "revolutions", e.g., that related to the "genetic" learning. As a result, in particular, even the most "reputable" connectionist histories of the field of pattern recognition, which was formed more than three decades ago and to which the connectionism properly belongs, show amazing ignorance of the major developments in the parent (pattern recognition) field: the emergence of two important and formally quite irreconcilable recognition paradigms--vector space and syntactic. The latter ignorance is even more instructive in view of the fact that many engineers who got involved with the field of pattern recognition through the connectionist "movement" are also ignorant of the above two paradigms that were discovered and developed within largely applied/engineering parent field of pattern recognition. As far as the inception of a scientific field is concerned, it should be quite clear that the initial choice of the basic scientific problem(s) is of decisive importance. This is particularly true for cognitive modeling where the path from the model to the experiment and the reverse path are much more complex than was the case, for example, at the inception of physics. In this connection, a very important question arises, which will be addressed in the special issue: What form will the future/adequate cognitive models take? Furthermore, may be, as many cognitive scientists argue, since we are in a prescientific stage, we should simply continue to collect more and more data and not worry about the future models. The answer to the last argument is quite clear to me: look very carefully at the "data" and the corresponding experiments and you will note that no data can be even collected without an underlying model, which always includes both formal and informal components. In other words, we cannot avoid models (especially in cognitive science, where the path from the model to the experiment will be much longer and more complex than is the case in all other sciences). Therefore, paraphrasing Friedrich Engels's thought on the role of philosophy in science, one can say that there is absolutely no way to do a scientific experiment without the underlying model and the difference between a good scientist and a bad one has to do with the degree to which each realizes this dependence and actively participates in the selection of the corresponding model. It goes without saying that, at the inception of the science, the decision on which cognitive process one must focus initially should precede the selection of the model for the process. As to the choice of the basic scientific problem, or basic cognitive process, it appears that the really central cognitive process is that of inductive learning, which might have been marked so by many great philosophers of the past four centuries (e.g., Bacon, Descartes, Pascal, Locke, Hume, Kant, Mill, Russell, Quine) and even earlier (e.g., Aristotle). The insistence of such outstanding physiologists and neurophysiologists as Helmholtz and Barlow on the central role of inductive learning processes is also well known. However, in view of the difficulties associated with developing an adequate inductive learning model, researchers in AI and to a somewhat lesser extent in cognitive science have decided to view inductive learning not as a central process at all, i.e., they decided to "dissolve" the problem. It became clear to me that the above difficulties are related to the development of a genuinely new (symbolic) mathematical framework that can SATISFACTORILY define the concept of INDUCTIVE CLASS REPRESENTATION (ICR), i.e., the nature of encoding essentially infinite data set on the basis of a small finite set. (The most known as well as critical to the development of mathematics example of ICR is that of the classical Peano representation of the set of natural numbers--one element plus one operation--used in mathematical induction.) Thus, the main differences between inductive learning models should be viewed in light of the differences between the formal means, i.e. mathematical structures, offered by various models for representing the class inductively. I will also argue (in one of the papers) that the classical mathematical (numeric) models, including the vector space and probabilistic models, offer inadequate axiomatic frameworks for capturing the concept of ICR. As Peter Gardenfors aptly remarked in his 1990 paper, "induction has been called 'the scandal of philosophy' [and] unless more consideration is given to the question of which form of knowledge representation is appropriate for mechanized inductive inferences, I'm afraid that induction may become a scandal of AI as well." I strongly believe that all attempts to "dissolve" the inductive learning processes are futile and, moreover, that these processes are central cognitive processes for all levels of processing, hence the earlier workshop in Toronto (May 20-21) under the same title and the present Special Issue. I invite all researchers seriously interested in the scientific foundations of cognitive science, AI, or pattern recognition to submit the papers addressing, in addition to other relevant issues, the following questions: * What is the role of mathematics in cognitive science, AI, and pattern recognition? * Are there any central cognitive processes? * What is inductive learning? * What is inductive class representation (ICR)? * Are there several basic inductive learning processes? * Are the inductive learning processes central? * What are the relations between inductive learning processes and the known physical processes? * What is the relationship between the measurement processes and inductive learning processes (e.g., retina as a structured measurement device)? * What is the role of inductive learning in sensation and perception (vision, hearing, etc.)? * What is the relation between the inductive learning, categorization, and pattern recognition? * What is the relation between the supervised/inductive learning and the unsupervised learning? * What is the role of inductive learning processes in language acquisition? * What are the relationships, if any, between the inductive class representation (ICR) and the basic object representation (from the class)? * What are the differences between the mathematical structures employed by the known inductive learning models for capturing the corresponding ICRs? * What is the role of inductive learning in memory and knowledge representation? * What are the relations, if any, between the ICR and mental models and frames? When preparing the manuscript, please conform to the standard submission requirements given in journal Pattern Recognition, which could be faxed or mailed if necessary. Hardcopies (4) of each submission should be mailed to Lev Goldfarb Faculty of Computer Science University of New Brunswick P.O. Box 4400 E-mail: goldfarb@unb.ca Fredericton, N.B. E3B 5A3 Tel: 506-453-4566 Canada Fax: 506-453-3566 by the SUBMISSION DEADLINE, August 20, 1996. The review process should take about 4-5 weeks and will take into account the relevance, quality, and originality of the contribution. Potential contributors are encouraged to contact me with any questions they might have. Lev Goldfarb http://wwwos2.cs.unb.ca/profs/goldfarb/goldfarb.html ------------------------------ Date: Fri, 7 Jun 1996 22:05:17 -0500 Subject: Applications of Neural Networks to Signal Processing From: Yu Hu ******************************************************************** * CALL FOR PAPERS * * * * A Special Issue of IEEE Transactions on Signal Processing: * * Applications of Neural Networks to Signal Processing * * * ******************************************************************** Expected Publication Date: November 1997 Issue Submission Deadline: December 1, 1996 Guest Editors: A. G. Constantinides, Simon Haykin, Yu Hen Hu, Jenq-Neng Hwang, Shigeru Katagiri, Sun-Yuan Kung, T. A. Poggio Significant progress has been made applying artificial neural network (ANN) techniques to signal processing. From a signal processing perspective, it is imperative to understand how the neural network based algorithms are related to more conventional approaches in terms of performance, cost, and practical implementation issues. Questions like these demand honest, pragmatic, innovative, and imaginative answers. This special issue offers a unique forum for researchers and practitioners in this field to present their view on these important questions. We seek highest quality manuscripts which focus on the signal processing aspects of a neural network based algorithm, applications or implementation. Topics of interests include, but are not limited to: .. Neural network based signal detection, classification, and understanding algorithms. .. Nonlinear system identification, signal prediction, modeling, adaptive filtering, and neural network learning algorithms. .. Neural network applications to biomedical signal processing, including medical imaging, Electrocardiogram, EEG, and related topics. .. Signal processing algorithms for biological neural system modeling .. Comparison of neural network based approach with conventional signal processing algorithms for solving real world signal processing tasks. .. Real world signal processing applications based on neural networks. .. Fast and parallel algorithms for efficient implementation of neural networks based signal processing systems. Prospective authors are encouraged to SUBMIT MANUSCRIPTS BY DECEMBER 1, 1996 to: Professor Yu-Hen Hu E-mail: hu@engr.wisc.edu Univ. of Wisconsin - Madison, Phone: (608) 262-6724 Dept. of Electrical and Computer Engineering Fax: (608) 262-1267 1415 Engineering Drive Madison, WI 53706-1691 U.S.A. On the cover letter, indicate the manuscript is submitted to the special issue on neural network for signal processing . All manuscripts should conform to the submission guideline detailed in the "information for authors" printed in each issue of the IEEE Transactions on Signal Processing. Specifically, the length of each manuscript should not exceed 30 double-spaced pages. SCHEDULE Manuscript received by: December 1, 1996 Completion of initial review: March 31, 1997 Final manuscript received by : June 30, 1997 Expected publication date: November, 1997 DISTINGUISHED GUEST EDITORS Prof. A. G. Constantinides, Imperial College, UK, a.constantinides@romeo.ic.ac.uk Prof. Simon Haykin, McMaster University, Canada, haykin@synapse.crl.mcmaster.ca Prof. Yu Hen Hu, Univ. of Wisconsin, U.S.A., hu@engr.wisc.edu Prof. Jenq-Neng Hwang, University of Washington, U.S.A., hwang@ee.washington.edu Dr. Shigeru Katagiri, ATR, JAPAN, katagiri@hip.atr.co.jp Prof. Sun-Yuan Kung, Princeton University, U.S.A., kung@princeton.edu Prof. T. A. Poggio, Massachusetts Inst. of Tech., U.S.A., tp-temp@ai.mit.edu ------------------------------ Date: Fri, 7 Jun 1996 15:29:12 -0600 (MDT) Subject: CfP ISFL'97: 2nd Int Symp on Fuzzy Logic, Feb 12-14,97, Zurich From: icsc@freenet.edmonton.ab.ca Upon multiple request the final deadline for ISFL'97 submissions has been extended to July 10, 1996. ISFL'97 Second International ICSC Symposium on FUZZY LOGIC AND APPLICATIONS Information: For further information, please contact either of the following: - ICSC Canada, P.O. Box 279, Millet, AB T0C 1Z0, Canada E-mail: icsc@freenet.edmonton.ab.ca Fax: +1-403-387-4329 Phone: +1-403-387-3546 - ICSC Switzerland, P.O. Box 657, CH-8055 Zurich, Switzerland Fax: +41-1-761-9627 - Prof. Nigel Steele, Chairman ISFL'97, Coventry University, U.K. E-mail: nsteele@coventry.ac.uk Fax: +44-1203-838585 Phone: +44-1203-838568 ------------------------------ End of ML-LIST (Digest format) **************************************** From alpaydin@boun.edu.tr Tue Jun 11 13:52:07 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id NAA02928 for ; Tue, 11 Jun 1996 13:51:56 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id NAA28189 for ; Tue, 11 Jun 1996 13:51:54 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa29348; 11 Jun 96 12:37:46 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa29345; 11 Jun 96 12:14:04 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa24978; 11 Jun 96 12:12:28 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa10343; 11 Jun 96 7:56:07 EDT Received: from hamlin.cc.boun.edu.tr by CS.CMU.EDU id aa06206; 11 Jun 96 7:55:44 EDT Received: by hamlin.cc.boun.edu.tr (AIX 4.1/UCB 5.64/4.03) id AA18096; Tue, 11 Jun 1996 14:33:41 +0400 Date: Tue, 11 Jun 1996 14:33:41 +0400 (MEDT) From: Ethem Alpaydin X-Sender: alpaydin@hamlin.cc.boun.edu.tr To: connectionists@cs.cmu.edu, ml@ics.uci.edu, kdd@gte.com, inductive@hermes.csd.unb.ca, DAI-List@ece.sc.edu, GA-List@aic.nrl.navy.mil, ai-stats@watstat.uwaterloo.ca, intcon@phoenix.ee.unsw.edu.au, EP-LIST@magenta.me.fau.edu, alife@cognet.ucla.edu, hybrid-list@cs.ua.edu, neuron@CATTELL.PSYCH.UPENN.EDU, cogpsy@neuro.psy.soton.ac.uk, genetic-programming@cs.stanford.edu, gann@cs.iastate.edu, evolutionary-computing@mailbase.ac.uk, TIERRA@life.slhs.udel.edu, cells@tce.ing.uniroma1.it, colt@cs.uiuc.edu Subject: Call for Participation: Tainn'96 In-Reply-To: Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII Pre-S. We're sorry if you receive multiple copies of this message. ase forward * Please post * Please forward * Please post * Please forwa Call for Participation TAINN'96, Istanbul 5th Turkish Symposium on Artificial Intelligence and Neural Networks To be held at Istanbul Technical University, Macka Campus June 27 - 28, 1996 Jointly-organized by Bogazici University and Istanbul Technical University Invited talk by Prof Teuvo Kohonen, Helsinki University of Technology Full program, registration and accommodation information can be e-received by Email: tainn96@boun.edu.tr URL: http://www.cmpe.boun.edu.tr/~tainn96 From listerrj@helios.aston.ac.uk Wed Jun 12 22:38:12 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id WAA00998 for ; Wed, 12 Jun 1996 22:37:56 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id PAA13922 for ; Wed, 12 Jun 1996 15:55:07 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa00423; 12 Jun 96 12:58:10 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa00421; 12 Jun 96 12:30:31 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa25917; 12 Jun 96 12:30:03 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa01930; 12 Jun 96 10:44:53 EDT Received: from email.aston.ac.uk by EDRC.CMU.EDU id aa24603; 12 Jun 96 10:43:37 EDT Received: from sun.aston.ac.uk (actually host dirac.aston.ac.uk) by email.aston.ac.uk with SMTP (PP); Wed, 12 Jun 1996 15:45:50 +0100 Message-Id: <4719.199606121441@sun.aston.ac.uk> To: Connectionists@cs.cmu.edu Subject: PhD Studentship Available X-Mailer: Mew version 1.02 on Emacs 19.29.2 Mime-Version: 1.0 Content-Type: Text/Plain; charset=us-ascii Date: Wed, 12 Jun 1996 15:41:56 +0100 From: Richard Lister ---------------------------------------------------------------------- Neural Computing Research Group ------------------------------- Dept of Computer Science and Applied Mathematics Aston University, Birmingham, UK PhD STUDENTSHIP AVAILABLE ------------------------- *** Full details at http://www.ncrg.aston.ac.uk/ *** A studentship exists for a project which is jointly funded by the UK EPSRC, and by British Aerospace under the Total Technology scheme. The student will be expected to follow the Neural Computing MSc by Research degree for the first year, and will also be expected to pass four modules from the MBA course. The funding covers tuition fees and living expenses for three years and the student is expected to gain a PhD in Neural Computing at the end of this period. The project supervisor will be Professor David Lowe and the studentship will be based at Aston University, Birmingham, UK. Structural Characterisation of Wake EEG Signals ----------------------------------------------- A student is required to carry out research in the interdisciplinary area of multichannel EEG signal characterisation using statistical pattern recognition and artificial neural networks. The aim of the project is to investigate the degree to which attentiveness or vigilance may be characterised through an analysis of wake EEG signals. The problem domain is one of extraction and interpretation of structure in an environment in which there is little or no labelled data and in which there is a poor signal to noise ratio. Macrostate unsupervised clustering of multivariate EEG data is a difficult problem area and requires a high level of competence across discipline boundaries. The project calls for developing skills in linear and non-linear signal processing, biomedical data interpretation, statistical clustering methodology and artificial neural networks. The student will have to be mathematically and computationally proficient. How to apply ------------ This award is made on a competitive basis and students should ensure that applications reach the Neural Computing Research Group at Aston University by Wednesday June 19th 1996. An electronic version of the application form, is available on our Web Pages at http://www.ncrg.aston.ac.uk/ . Interviews will be held on Friday 21st June 1996 and candidates must ensure that they are available for interview. Successful candidates will be notified by telephone and/or email if they are to be called for interview. Candidates will be notified of the outcome on Monday 24th June 1996. ---------------------------------------------------------------------- From weissg@informatik.tu-muenchen.de Thu Jun 13 02:06:45 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id CAA08012 for ; Thu, 13 Jun 1996 02:06:34 -0500 Received: from cs.uwa.oz.au (bilby.cs.uwa.oz.au [130.95.1.11]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id CAA20721 for ; Thu, 13 Jun 1996 02:06:27 -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 LAA25292; Thu, 13 Jun 1996 11:53:36 +0800 Message-Id: <199606130353.LAA25292@cs.uwa.oz.au> From: Gerhard Weiss Sender: Gerhard Weiss To: reinforce@cs.uwa.edu.au Subject: CFP: MLJ Special Issue on Multiagent Learning Date: Wed, 12 Jun 1996 16:49:34 +0200 (MET DST) =========================================================================== CALL FOR PAPERS Special Issue on MULTIAGENT LEARNING of the Journal MACHINE LEARNING guest-edited by Michael Huhns and Gerhard Weiss -- http://www7.informatik.tu-muenchen.de/~weissg/si-mlj -- Topic and Goal -------------- Multiagent learning, that is, learning that relies on or even requires the interaction between several computational agents, establishes a relatively young but significant topic in artificial intelligence. The goal of this special issue is to increase awareness of this topic, and to serve as a basis for stimulating further research. We encourage submission of un- published high-quality papers that describe mature work and reflect the state of the art in multiagent learning. Papers that present work centered around the question how multiple agents can collectively learn to solve complex tasks are particularly welcome. Submitted papers should address realistic computational constraints on multiagent learning, and offer a detailed and conclusive experimental and/or theoretical analysis of their described approaches. Submission Information ---------------------- Papers should be double spaced and 8,000 to 12,000 words in length, with full-page figures counting for 400 words. All submissions will be subject to the standard review procedure; the review process will consider the originality and significance of the contribution, quality of evaluation, and clarity of presentation. The first page should include the title, abstract, key words, and author information (name, affiliation, mailing address, telephone number, and e-mail address). The text of the paper should begin on the second page and continue on consecutively numbered pages. For more information on the format, consult the inside back cover of a recent issue of Machine Learning. (Note that Machine Learning accepts submission of final copy in electronic form. There is a latex style file and related files available via anonymous ftp from world.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.) Authors should submit six (6) copies of papers as indicated: Four (4) hard copies to: Karen Cullen MACHINE LEARNING Editorial Office Kluwer Academic Publishers 101 Philip Drive Norwell, MA 02061 USA One hard copy to each Guest Editor (2): Michael Huhns Center for Information Technology Department of Electrical & Computer Engineering University of South Carolina Columbia, SC 29208 USA Gerhard Weiss Institut fuer Informatik Technische Universitaet Muenchen D-80290 Muenchen, Germany We encourage potential authors to contact Michael Huhns (huhns@sc.edu) or Gerhard Weiss (weissg@informatik.tu-muenchen.de) prior to submission if they have questions. Important Dates --------------- Submission deadline: 1 February 1997 Acceptance notification: 1 May 1997 Final manuscript: 1 September 1997 Planned publication date: 1 January 1998 =========================================================================== From adali@engr.umbc.edu Thu Jun 13 15:10:08 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id PAA00323 for ; Thu, 13 Jun 1996 15:10:00 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id PAA00838 for ; Thu, 13 Jun 1996 15:09:58 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa01628; 13 Jun 96 15:08:55 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa01626; 13 Jun 96 14:45:59 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa26904; 13 Jun 96 14:45:25 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa04395; 13 Jun 96 12:17:47 EDT Received: from coe1.engr.umbc.edu by EDRC.CMU.EDU id aa00910; 13 Jun 96 12:17:11 EDT Received: from akdeniz.engr.umbc.edu (adali@akdeniz.engr.umbc.edu [130.85.110.116]) by coe1.engr.umbc.edu (8.6.12/8.6.9) with ESMTP id LAA26465 for ; Thu, 13 Jun 1996 11:18:49 -0400 From: Tulay Adali Received: (adali@localhost) by akdeniz.engr.umbc.edu (8.6.12/8.6.5) id QAA06071 for connectionists@cs.cmu.edu; Thu, 13 Jun 1996 16:17:01 GMT Message-Id: <199606131617.QAA06071@akdeniz.engr.umbc.edu> Subject: CFP: Spec. Issue on Apps. of NNets in Biomedical Imaging/Image Processing To: connectionists@cs.cmu.edu Date: Thu, 13 Jun 1996 12:17:00 -0400 (EDT) X-Mailer: ELM [version 2.4 PL23] MIME-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Length: 2034 --------------------------------------------------------------------- CALL FOR PAPERS --------------------------------------------------------------------- Special Issue on Applications of Neural Networks in Biomedical Imaging/Image Processing --------------------------------------------------------------------- We invite papers on applications of artificial neural networks in biomedical imaging and biomedical image processing to appear in a special issue of the Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology. Some possible areas of application are (but not restricted to): pattern recognition and feature extraction for computer aided diagnosis and prognosis, analysis (quantification, segmentation, edge detection, etc.), restoration, compression, registration, reconstruction, and quality evaluation of medical images. Of particular interest are methods that take advantage of the multimodal nature of biomedical image data which is available in most studies as well as techniques developed for sequences of biomedical images such as those for dynamic PET and functional MRI data. Schedule: Manuscript submission deadline: August 15, 1996 Notification of acceptance: January 15, 1997 Final manuscript submission deadline: March 1, 1997 Expected publication date: Third quarter of 1997 Prospective authors should follow the regular guidelines for publications submitted to journals of Kluwer Academic Publishers except that the manuscripts should be submitted to Tulay Adali, guest editor of the special issue. Submission instructions for the journal can be found at http://www.kwap.nl. Guest Editor: TULAY ADALI Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore, MD 21228-5398 Tel: (410) 455-3521 Fax: (410) 455-3969 E-mail: adali@engr.umbc.edu For updates and a list of references in the area refer to: http://www.engr.umbc.edu/~adali/biomednn.html. \end{document} From csj@ccms.ntu.edu.tw Thu Jun 13 21:50:58 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id VAA06854 for ; Thu, 13 Jun 1996 21:50:42 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id VAA06049 for ; Thu, 13 Jun 1996 21:50:40 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa01979; 13 Jun 96 19:06:06 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa01977; 13 Jun 96 18:58:24 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa27014; 13 Jun 96 18:57:45 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa24656; 13 Jun 96 3:35:38 EDT Received: from ccms.ntu.edu.tw by EDRC.CMU.EDU id aa28922; 13 Jun 96 3:34:52 EDT Received: (from csj@localhost) by ccms.ntu.edu.tw (8.6.10/8.6.9) id PAA24823 for Connectionists@CS.CMU.EDU; Thu, 13 Jun 1996 15:28:34 +0800 Date: Thu, 13 Jun 1996 15:28:34 +0800 From: Sao-Jie Chen Message-Id: <199606130728.PAA24823@ccms.ntu.edu.tw> To: Connectionists@cs.cmu.edu Subject: call for papers: ANNCSSP'96 (Taiwan) Submitted by : Prof. Von-Wun Soo ************************************************************************ SECOND CALL FOR PAPERS 1996 International Symposium on Multi-Technology Information Processing A Joint Symposium of Artificial Neural Networks, Circuits and Systems, and Signal Processing December 16-18, 1996 Hsin-Chu, Taiwan, Republic of China ************************************************************************ Submission of extended summary by July 15, 1996. Sponsored by: National Tsing Hua University (NTHU) Ministry of Education, Taiwan R.O.C. National Science Council, Taiwan R.O.C. in Cooperation with IEEE Signal Processing Society IEEE Circuits and Systems Society (pending) IEEE Neural Networks Council IEEE Taiwan Section Taiwanese Association for Artificial Intelligence ORGANIZATION General Co-chairs: H. C. Wang, NTHU, Y. H. Hu, U. of Wisconsin Advisory board Co-chairs: W. T. Chen, NTHU S. Y. Kung, Princeton U. Vice Co-chairs: H. C. Hu, NCTU J. N. Hwang, U. of Washington Program Co-chairs: V. W. Soo, NTHU C. H. Lee, AT&T Call For Papers The International Symposium on Multi-Technology Information Processing (ISMIP'96), a joint symposium of artificial neural networks, circuits and systems, and signal processing, will be held in National Tsing Hua University, Hsin Chu, Taiwan, Republic of China. This conference is an expansion of previous series of International Symposium of Artificial Neural Networks (ISANN). The main purpose of this conference is to offer a forum showcasing the latest advancement of modern information processing technologies. It will include recent innovative research results of theories, algorithms, architechtures, systems, hardware implementations that lead to intelligent information processing. The technical program will feature opening keynote addresses, invited plenary talks, technical presentations of refereed papers. The official language is English. Papers are solicited for, but not limited to, the following topics: 1. Associative Memory 2. Digital and Analog Neurocomputers 3. Fuzzy Neural Systems 4. Supervised/Unsupervised Learning 5. Robotics 6. Sensory/Motor Control 7. Image Processing 8. Pattern Recognition 9. Langauge/ Speech Processing 10. Digital Signal Processing 11. VLSI Architectures 12. Non-linear Circuits 13. Multimedia information processing 14. Optimization 15. Mathematical Methods 16. Visual signal processing 17. Content based signal processing 18. Applications Prospective authors are invited to submit 4 copies of extended summaries of no more than 4 pages. All the manuscripts must be written in English in single-spaced, single column, on 8.5" by 11" white papers. The top of the first page of the paper should include a title, authors' names, affiliations, address, telephone/fax numbers, and email address if applicable. The indicated corresponding author will receive an acknowledgement of his/her submission. Camera-ready full papers of accepted manuscripts will be published in a hard-bound proceedings and distributed in the symposium. For more information, please consult at the URL site http://pierce.ee.washington.edu/~nnsp/ismip96.html Authors are invited to send submissions to one of the program co-chairs: For submissions from USA and Europe: Dr. Chin-Hui Lee Bell Labs, Lucent Technologies 600 Mountain Avenue Murray Hill, NJ 07974, USA E-Mail: chl@research.bell-labs.com Phone: 908-582-5226 Fax: 908-582-7308 For submissions from Asia and the rest of the world Prof. V. W. Soo Dept. of Computer Science National Tsing Hua University Hsin Chu, Taiwan 30043, ROC E-Mail: soo@cs.nthu.edu.tw Phone: 886-35-731068 Fax: 886-35-723694 Schedule Submission of extended summary: July 15, 1996. Notification of acceptance: September 30, 1996. Submission of camera-ready paper: October 31, 1996. Advanced registration, before: November 15, 1996. From marshall@cs.unc.edu Fri Jun 14 12:28:08 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id MAA21329 for ; Fri, 14 Jun 1996 12:27:56 -0500 Received: from cs.uwa.oz.au (bilby.cs.uwa.oz.au [130.95.1.11]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id MAA15048 for ; Fri, 14 Jun 1996 12:27:49 -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 UAA02686; Fri, 14 Jun 1996 20:08:55 +0800 Message-Id: <199606141208.UAA02686@cs.uwa.oz.au> From: Jonathan Marshall To: cogneuro@ptolemy-ethernet.arc.nasa.gov, cogni-info@univ-lyon1.fr, cogpsy@phil.ruu.nl, cogpsych@ripken.oit.unc.edu, connectionists@cs.cmu.edu, duke-talks@cs.duke.edu, inns-l@umdd.umd.edu, issnnet-mlist@cns.bu.edu, neuron@cattell.psych.upenn.edu, nl-kr@cs.rochester.edu, psyc@pucc.princeton.edu, psycgrad@acadvm1.uottawa.ca, reinforce@cs.uwa.edu.au, tanns@cs.unc.edu Subject: Society for Math Psych conference program -- Chapel Hill, NC [connectionists] Date: Thu, 13 Jun 1996 20:13:29 -0400 ------------------------------------------------------------------------------ 29th Annual Meeting of the Society for MATHEMATICAL PSYCHOLOGY 1-4 August 1996, University of North Carolina, Chapel Hill, NC Dear Colleague, It is our pleasure to invite you to the University of North Carolina at Chapel Hill for the 29th ANNUAL MEETING OF THE SOCIETY FOR MATHEMATICAL PSYCHOLOGY. The meeting will begin on the morning of August 2 and continue until mid-day on August 4, 1996. We have arranged an informal meeting place during the evening of August 1 for participants who arrive on that day. On behalf of Ido Erev, David Budescu, and Rami Zwick, we also invite you to a WORKSHOP ON GAMES AND BEHAVIOR in honor of Amnon Rapoport that will immediately follow the Mathematical Psychology Meeting. The workshop will begin on the afternoon of Sunday, August 4 and will conclude on the afternoon of Monday, August 5. The submitted abstracts suggest excellent meetings, and we hope you will be able to attend. An innovation to the Mathematical Psychology Meeting this year is the addition of a poster session each afternoon in conjunction with a long break in the paper sessions. Consequently, there will be many more presentations than usual, but without disrupting the relaxed flow of activities. Please register by the July 22 deadline in order to help us plan properly. If you wish to arrange hotel or dorm accommodations, please do so before July 15. Attached to this letter you will find preliminary programs for both meetings, registration information and forms, as well as information regarding dormitory and hotel accommodations, how to reach Chapel Hill, and the general Chapel Hill area. Information about the conference will also be available at http://www.cs.unc.edu/~marshall/math.html on the World Wide Web. We are looking forward to seeing you! Sincerely yours, Thomas S. Wallsten and Jonathan A. Marshall Conference Co-Chairs ------------------------------------------------------------------------------ Preliminary Program 29th Annual Meeting of the Society for MATHEMATICAL PSYCHOLOGY 1-4 August 1996, University of North Carolina, Chapel Hill, NC Sponsors: University of North Carolina at Chapel Hill (College of Arts and Sciences, Department of Psychology, Department of Computer Science); Society for Mathematical Psychology; Triangle Area Neural Network Society. Organizing Committee: Christina A. Burbeck, Elliot Hirshman, Jonathan A. Marshall (Co-Chair), Nestor Schmajuk, Thomas S. Wallsten (Co-Chair), Yiu-Fai Yung. Keynote Speakers: Stephen Grossberg (Boston U), In Jae Myung (Ohio State U), Amnon Rapoport (U of Arizona). Special Symposia: - Games and Behavior - Models of Binding Mechanisms in Vision ------------------------------------------------------------------------------ THURSDAY 01 AUGUST 1996 6:00-9:00 p.m. INFORMAL EVENING MEETING PLACE. Crossroads Bar, Carolina Inn. ------------------------------------------------------------------------------ FRIDAY 02 AUGUST 1996, Sitterson Hall, rooms 011 and 014 8:00 REGISTRATION. Sitterson Hall, lower lobby. SENSATION & PERCEPTION LEARNING & MEMORY 8:30 RG Swensson, PF Judy, Harvard U. B Murdock, U of Toronto. Recall, Observer detection efficiency for judgments of frequency, and visual targets at specified or judgments of recency in TODAM2. unknown locations in noise backgrounds. 8:55 ED Reichle, A Pollatsek, DJK Mewhort, DG Smith, R Kohly, DL Fisher, K Rayner, U of Mass, Queen's U. Interference in memory Amherst. A model of eye produces numerical distance movements in reading. effects. 9:20 D Levin, U of Chicago. A MJ Kahana, Brandeis U. Temporal relativistic description of coding in human memory. perception. 9:45 K Loubier, Z Pizlo, Purdue U. M Howard, MJ Kahana, Brandeis U. Shape constancy in the case of a Mathematical model of free recall single perspective view of a memory. solid object. 10:10 BREAK BREAK Friday 02 August (continued) SENSATION & PERCEPTION LEARNING & MEMORY 10:30 TM Cowan, Kansas State U. DM Riefer, M LaMay, Cal State U, Replotting corda tympani data and San Bernardino. Memory for its implications for theories of common and bizarre imagery: A taste. storage-retrieval analysis. 10:55 EA Roy, M Hollins, U of North TD Wickens, U of Cal, Carolina, Chapel Hill. A model Los Angeles. Forgetting as a of vibrotactile loudness. failure process. 11:20 J Doner, Charlottesville, VA. X Hu, W Marks, A Isenberg, State and spatial complementarity U of Memphis. Retrieval inhibi- in the dipole information of tion in directed forgetting: A discrete 2D patterns. source monitoring approach. 11:45 T Indow, U of Cal, Irvine. J Metcalfe, R Dodhia, Columbia U. Mathematical implication of Source monitoring in a composite Munsell color system. memory model. 12:10 LUNCH. Nearby restaurants. LUNCH. Nearby restaurants. INFORMATION PROCESSING & MEASUREMENT & STATISTICS PERFORMANCE 1:25 JD Balakrishnan, R Venugopalan, MJ Wenger, JT Townsend, Purdue U. Fixed and variable Indiana U. Facial gestalts and sample, distribution-free configurality as aspects of form measures of response bias. and capacity. 1:50 Y Yung, U of North Carolina, JT Townsend, MJ Wenger, Chapel Hill. Applications of the Indiana U. Evidence monitoring bootstrap to structural equation theory: A dynamic extension of modeling: Techniques and issues. general recognition theory and cognitive stochastic processing theory. 2:15 C Chiang, James Madison U. R Ratcliff, G McKoon, Invariant parameters of Northwestern U. A counter model measurement scales. for implicit priming in perceptual identification. Friday 02 August (continued) 2:40 BREAK AND POSTERS RD Thomas, DP Gallogly, Miami U. Some consequences of the RT-distance hypothesis on factorial additivity. SA Marinov, Applied Linguistics Centre, Winnipeg. Pseudo-physical dimensions in analysis of psychological data. MA Woodbury, KG Manton, Duke U. Grade of membership analysis: Insights for measurement theory. AB Cobo-Lewis, U of Miami. An adaptive method for estimating multiple parameters of a psychometric function. J Miles, M Shevlin, Derby U, Nottingham Trent U. How to Excel at SEM. K Tateneni, MW Browne, Ohio State U. A noniterative algorithm for joint correspondence analysis. K Hayashi, PK Sen, U of North Carolina, Chapel Hill. Covariance matrix for covariance estimators of MLEs of factor loadings with raw-varimax rotation in factor analysis. SM Zoldi, AD Krystal, HS Greenside, Duke U. Statistical analysis of redundancy and stationarity in multi-channel EEG. GJ Kalarickal, JA Marshall, U of North Carolina, Chapel Hill. Synaptic plasticity: Comparison of EXIN and BCM learning rules. C Kim, IJ Myung, WB Levy, Ohio State U, U of Virginia. Encoding of context information with self-organizing neural assemblies. RA Heath, U of Newcastle. Controlling a chaotic neural network: Implications for decision making, memory, and motor control. AR Sarkisyan, HH Mkrtchian, AA Melkonian, Nat Acad of Sciences of Armenia. A method for cable model parameters identification using output frequency characteristics. S Graham, Z Pizlo, A Joshi, Purdue U. An exponential pyramid model of solving the traveling salesman problem. MA Garcia-Perez, U Complutense, Madrid. Forced-choice staircases: Some little known facts. AK Hon, LT Maloney, New York U. Analysis of visual interpolation and segmentation of sampled contours using Frechet derivatives. GL Zimmerman, MR Canaday, Tulane U. Adaptive model of equiluminant motion perception. 3:40 PLENARY TALK: Stephen Grossberg, Boston U. The attentive brain: Perception, learning, and consciousness. Friday 02 August (continued) -- Saturday 03 August NON-LABORATORY APPLICATIONS NEURAL & LEARNING SYSTEMS 4:40 DJ Weiss, CS Rundall, Cal NA Schmajuk, C Buhusi, JA Gray, State U, Los Angeles. Using Duke U, Inst of Psychiatry nested group designs to examine (London). An attentional- subject characteristics in configural model of the cognitive models. classical conditioning. 5:05 RL Stout, Brown U. Analyzing out- J Zhang, M Chang, U of Michigan. come over time in Project MATCH. A model of operant reinforcement learning. 5:30 J Shanteau, Kansas State U. The RM Golden, U of Texas, Dallas. psychometrics of expertise Mathematical methods for revisited. connectionist model analysis and design. 5:55 SESSION END BANQUET DINNER -- Carolina Club. ------------------------------------------------------------------------------ SATURDAY 03 AUGUST 1996, Sitterson Hall, rooms 011 and 014 JUDGMENT & DECISION MAKING LEARNING & MEMORY 8:30 A Diederich, U Oldenburg. Get T Van Zandt, Johns Hopkins U. MADD: Decision making in conflict Confidence judgments in situations. recognition memory: A two-choice decision is not the same as a six-choice decision. 8:55 RH Bender, U of North Carolina, RA Chechile, Tufts U. Unifica- Chapel Hill. Extending and test- tion of signal-detection and ing the stochastic judgment model process-tree approaches for in a four-category rating task. memory storage measurement. 9:20 M Regenwetter, AAJ Marley, D Nikolic, SD Gronlund, U of McGill U. Random relations, Oklahoma. A tandem random walk random utilities, and random model for the speed-accuracy functions. tradeoff paradigm. 9:45 AAJ Marley, R Regenwetter, H Joe, JR Busemeyer, E Byun, E Delosh, McGill U, U of British Columbia. M McDaniel, Purdue U. Random utility threshold models Artificial neural network models of subset choice. of function learning. 10:10 BREAK BREAK 10:30 PLENARY TALK: In Jae Myung, Ohio State U. Maximum entropy charac- terization of categorization models. (SMP New Investigator Award.) Saturday 03 August (continued) REACTION TIME CATEGORIZATION 11:30 R Schweickert, Purdue U. Response RM Nosofsky, TJ Palmeri, time distribution: Some simple Indiana U, Vanderbilt U. effects of factors selectively Comparing exemplar-retrieval and influencing mental processes. decision-bound models of speeded classification. 11:55 EN Dzhafarov, U of Illinois, WT Maddox, WK Estes, Arizona Urbana-Champaign. A canonical State U, Harvard U. A dual- representation for selectively process architecture for models influenced processes and of category learning. component times. 12:20 LUNCH. Nearby restaurants. LUNCH. Nearby restaurants. JUDGMENT & DECISION MAKING CATEGORIZATION 1:35 Y Li, DH Krantz, Columbia U. CJ Bohil, WT Maddox, Arizona Overconfidence and the goals of State U. Base-rate and payoff interval estimation. effects in multidimensional perceptual categorization. 2:00 DV Budescu, A Rapoport, U of FG Ashby, W Schwarz, U of Cal, Illinois at Urbana-Champaign, Santa Barbara. A stochastic U of Arizona. Randomization in version of general recognition individual choice behavior. theory. 2:25 Y Cho, RD Luce, G Fisher, PM Berretty, FG Ashby, S Queller, R Sneddon, U of Cal, Irvine. U of Cal, Santa Barbara. On the Certainty equivalents and joint dominance of verbal rules in receipt: Troubles and a possible unsupervised categorization. resolution. 2:50 P Wakker, H Zank, U of Tilburg, B Edelman, D Valentin, H Abdi, U of Limburg. Additive conjoint U of Texas, Dallas. Sex measurement for infinite product classification of face areas: sets: State-dependent expected Performance of human subjects and utility for decision under a linear neural network. uncertainty. Saturday 03 August (continued) 3:15 BREAK AND POSTERS CM Mayenga, U of Toronto. Dual scaling of sorting data: Determining dominant mathematical classificatory criterion. TJ Palmeri, Vanderbilt U. Exemplar similarity and the development of automaticity. C Sheu, D Kuhn, G Grams, DePaul U. On dividing the loot and claiming the debts. AW MacRae, U of Birmingham. The computer as opponent in experimental games. M Zlotnick, Washington, DC. A model for rational decision making and role-playing games. T Slembeck, U of St Gallen. Learning as a basic process in economic behavior: On the foundations of an economic theory of learning. TS Wallsten, H Gu, U of North Carolina, Chapel Hill. Effects of criterion variance on judgment: Model and data. M Chang, J Zhang, U of Michigan. An information-based model of choice reaction time. JF McGrew, Pacific Bell. Decision making in real-world situations: A model of the use of decision techniques by managers. SL Coleman, V Brown, DS Levine, U of Texas, Arlington. Foraging decisions made under risk: A cognitive-emotional neural network model. E Hirshman, U of North Carolina, Chapel Hill. A single-process model of the remember-know paradigm. E Fulcher, Worcester Coll of Higher Ed. Testing neural network models of conditioning within the human evaluative conditioning paradigm. ME Hasselmo, BP Wyble, Harvard U. A network model of the hippocampus that addresses human memory performance on delayed free recall and recognition under scopolamine. NG Kim, U of Connecticut, Storrs. Look in the direction of heading. K Niall, Armstrong Lab. Acuity of vision and the projective invariants of conics. Saturday 03 August (continued) REACTION TIME SENSATION & PERCEPTION 4:15 H Colonius, W Ellermeier, M Chen, KC Chen, SUNY Brockport, U Oldenburg, U Regensburg. Rochester Inst of Tech. A group Distribution inequalities for model of plane affine parallel models of reaction time transformations and perception. with generalized stopping rules. 4:40 PL Smith, U of Melbourne. Z Pizlo, E Weg, Purdue U. The Dynamic signal detection models concept of group, the likelihood driven by white noise integrals. principle, and the theory of shape constancy. 5:05 JN Rouder, Northwestern U. LT Maloney, P Mamassian, Assessing the roles of change New York U. The mother of all discrimination and accumulation: constancies: Effect of changes Evidence for a hybrid model of of illumination and material perceptual decision making. surface on perceived geometry of real objects. 5:30 SMP BUSINESS MEETING SMP BUSINESS MEETING 6:00 SESSION END. Dinner at nearby restaurants. ------------------------------------------------------------------------------ SUNDAY 04 AUGUST 1996, Sitterson Hall, rooms 011 and 014 8:30 PLENARY TALK: Amnon Rapoport, U of Arizona. Equilibrium play in large group noncooperative market entry games. SYMPOSIUM: MODELS OF BINDING IN VISION SYMPOSIUM: GAMES AND BEHAVIOR 9:30 M Kubovy, D Cohen, J Hollier, R Zwick, A Rapoport, E Weg, Hong U of Virginia, U of North Kong U of Sci & Tech, Carolina at Wilmington. The in- U of Arizona, Purdue U. A break- teraction of modules demonstrated down of invariance: The case of using the Gestalt detection two vs. three-person sequential technique: The pre-attentive bargaining. binding of color and form. 9:55 S Niyogi, MIT. Perceptual A Chaudhuri, Rutgers U. The structural descriptions, ratchet principle in a principal selection, and mental actions. agent game with unknown costs: An experimental analysis. 10:20 BREAK BREAK 10:40 J Feldman, Rutgers U. The logic Y Bereby-Meyer, Technion. A of perceptual grouping. reference-point model for the payoff effect in probability learning experiments. 11:05 JA Marshall, CP Schmitt, J Meyer, D Gopher, Ben Gurion U, GJ Kalarickal, RK Alley, Technion. Applying cognitive U of North Carolina, Chapel Hill. game theoretical analyses to Neural model of transfer-of- two-person signal detection. binding in visual relative motion perception. 11:30 J Zhang, U of Michigan. A S Gilat, J Meyer, D Gopher, Tech- geometric framework for nion, Ben Gurion U. Beyond perceptual binding. Bayes' theorem: The effect of base rate information in consensus games. 11:55 S Shams, Hughes Research Labs. A R Barkan, D Zohar, Technion. self-organizing model for solving Accidents and decision making the binding problem using under risk: A comparison of four nonlinear integrate-fire neurons. alternative models. 12:20 DISCUSSION SYMPOSIUM END 12:45 SYMPOSIUM END ------------------------------------------------------------------------------ Tentative Program: WORKSHOP ON GAMES AND BEHAVIOR in Honor of Amnon Rapoport's 60th birthday 4-5 August 1996, University of North Carolina, Chapel Hill, NC SUNDAY 04 AUGUST 1996, Sitterson Hall, room 011 LEARNING: 2:00-3:45 Colin Camerer, Teck-Hua Ho. Reinforcement learning with implicit beliefs and sophistication. Reinhard Selten. Learning direction theory. Al Roth, Ido Erev. A cognitive game theoretical analysis of learning in matrix games. SOCIAL DILEMMAS AND COOPERATION: 4:15-6:35 Gary Bornstein. Team games. Ramzi Suleiman, David Budescu. Common pool resource dilemmas with incomplete information. David M. Messick, Wim B. G. Liebrand. Levels of analysis and the explanation of the costs and benefits of cooperation. Robyn Dawes. A discussion of social dilemma research. BANQUET DINNER MONDAY 05 AUGUST 1996, Sitterson Hall, room 011. BIDDING, GUESSING AND BARGAINING: 8:30-10:15 John Kagel. Bidding in common value auctions: Why don't very experienced bidders earn even more? Rosemarie Nagel, John Duffy. On the robustness of behavior in experimental guessing games. Eythan Weg, Rami Zwick. Infinite horizon bargaining with complete and common knowledge: Facts and fictions. COORDINATION: 10:45-12:30 Daryl Seale, Jim Sundali. Strategic signaling in an N person simultaneous market entry game. John Van Huyck. Learning mutually consistent behavior. Jack Ochs, Jouh Duffy. Experimental study of the evolution of money. FAIRNESS AND TRUST: 2:00-3:45 Gary Bolton, Klaus Abbink, Abdolkarim Sadrieh, Fang-Fang Tang. Adaptive learning versus punishment in ultimatum bargaining. Chris Snijders, Gideon Keren. Determinants of trust. Warner Guth. On the effects of the pricing rule in auction and fair division games: An experimental study. ------------------------------------------------------------------------------ 29th Annual Meeting of the Society for Mathematical Psychology 2-4 August 1996 and Workshop on Games and Behavior in honor of Amnon Rapoport 4-5 August 1996 University of North Carolina at Chapel Hill Sponsors: The University of North Carolina at Chapel Hill (College of Arts and Sciences, Department of Psychology, Department of Computer Science); Society for Mathematical Psychology; Triangle Area Neural Network Society. GENERAL INFORMATION The Society for Mathematical Psychology Annual Meeting will run from 8:30 a.m. on Friday, 2 August, through 12:45 p.m. on Sunday, 4 August. In addition to oral and poster presentations, there will be a banquet on Friday, 2 August, and the annual business meeting of the Society on Saturday, 3 August. Please see the detailed preliminary program included with these materials. The conference will be followed by a special Workshop on Games and Behavior being held to honor the contributions of Amnon Rapoport. The workshop begins on 4 August 1996, after the conclusion of the Society Meeting. Participants in the SMP Annual Meeting are invited to attend. Please see the preliminary program included with these registration materials. Arrangements for the 1996 SMP Annual Meeting and the Workshop are being handled by the Conferences and Institutes Office in the Division of Continuing Education at UNC-Chapel Hill. Correspondence should be addressed to SMP Annual Meeting, Division of Continuing Education, CB 1020, The Friday Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-1020, USA. Phone 919-962-2643 or 800-845-8640, fax 919-962-2061, e-mail smp96@cs.unc.edu. LOCATION Conference sessions will be held in Sitterson Hall on the campus of the University of North Carolina at Chapel Hill. A pre-conference meeting place has been arranged for Thursday evening, 1 August, in the Crossroads Bar of the Carolina Inn, adjacent to campus. The banquet will be held at the Carolina Club, located on campus immediately across from Carmichael Hall, on Friday evening, 2 August. The Workshop on Games and Behavior will be held in Sitterson Hall. The Workshop dinner on Sunday evening, 4 August, will be held at Pyewacket's, a restaurant within walking distance of campus. Chapel Hill, home to the University, is a busy, modern town that maintains a village atmosphere. The Triangle area, which includes Raleigh, Durham, and Chapel Hill, offers museums, performing arts centers, historical and architectural landmarks, restaurants, shops of all descriptions, and sporting events. In addition to The University of North Carolina at Chapel Hill, the Triangle is home to Duke University, North Carolina State University, North Carolina Central University, and several private colleges. In August, the weather in Chapel Hill is typically hot and humid. REGISTRATION INFORMATION To register for the 29th Annual Meeting of the Society for Mathematical Psychology or for the Workshop on Games and Behavior, complete the form included with these materials. Mail the form, along with payment, to the address indicated. Registrations received after 22 July 1996, will be charged a $15 late fee. Hotel/dorm reservations must be made by July 15. The registration fees for the SMP Annual Meeting on 2-4 August are: SMP member, includes banquet (by 22 July 1996) $90 Non-member registration, includes banquet (by 22 July 1996) $95 Student registration, includes banquet (by 22 July 1996) $45 The registration fee includes all educational sessions, refreshment breaks, and the Friday banquet. Guests who wish to attend the banquet may purchase a ticket for $35; payment must accompany the participant registration. The registration fees for the Rapoport Workshop on Games and Behavior on 4-5 August are: Regular registration (by 22 July 1996) $35 Registration and proceedings, when available (by 22 July 1996) $142 In addition, individuals attending the Rapoport Workshop may register for the Saturday SMP sessions for $26 and for the Sunday SMP sessions for $13. Fees may be paid by check or money order (made payable to the Division of Continuing Education), purchase order, Visa, or MasterCard. Credit card and purchase order registrations may also be sent by phone, fax, or e-mail. For security reasons, we do not recommend sending credit card numbers by e-mail; those choosing to register by e-mail may telephone to provide the appropriate credit card information. The University of North Carolina at Chapel Hill maintains a policy of equal educational opportunity. CANCELLATIONS AND REFUNDS Requests for refunds will be honored if received in writing by 29 July 1996. No refunds are available after that date. Substitutions may be made at any time. SPECIAL NEEDS Individuals with special requirements to accommodate a motor or sensory impairment should indicate their needs in the space provided on the registration form. Information must be received by 22 July 1996. ACCOMMODATIONS A block of rooms has been reserved at the Best Western University Inn and at the Carolina Inn. The University Inn, located on Raleigh Road (Highway 54) approximately three miles east of campus, offers a complimentary continental breakfast with a full-service nearby restaurant serving lunch and dinner. Room rates at the University Inn are $65, plus tax, single or double. Rooms will be available at these rates until 15 July 1996. Please make your reservation by calling the motel directly at 919-932-3000, and identifying yourself as participant in the Society for Mathematical Psychology conference. The Carolina Inn, a recently renovated historic hotel, is located adjacent to Sitterson Hall. Room rates at the Carolina Inn are $79-$89, plus tax, single or double. Rooms will be available at this rate until 15 July 1996. Please make a reservation by calling the Inn directly at 919-933-2721 or 800-962-8519, and identifying yourself as a participant in the Society for Mathematical Psychology conference. Accommodations are also available in Carmichael Hall, an air-conditioned, smoke-free residence hall on campus. Linens (two sheets, one pillowcase, two towels, one washcloth, one pillow, one blanket) are included. Participants should bring an alarm clock as one will not be provided. Housekeeping service is not included. The cost for residence hall accommodations is $37 per night, single, or $44 per night, double, which includes a continental breakfast. All dormitory rooms must be vacated by Monday, 5 August. All reservations for Carmichael Hall must be pre-paid; reservation and payment must be received by 15 July 1996. Please mark the appropriate spot on the enclosed registration form. Parking permits will be available for purchase at check-in. TRAVELING TO CHAPEL HILL By automobile: Chapel Hill is easily accessible by car from Interstate 40 and from Interstate 85. Maps with directions and parking information will be mailed with the confirmation packet. By air: The University is approximately 15 miles from Raleigh-Durham International Airport (RDU). Ground transportation is available by cab, rental car, or airport shuttle service. LTD Transportation (800-432-8008 from the USA or 919-840-1836) and R&G Transportation (800-840-2738 from the USA or 919-840-0262) provide shuttle service to Chapel Hill. Both have information booths at the baggage claim areas of the terminals. Participants are encouraged to call in advance to schedule service. When calling, be prepared to provide a flight number and arrival time. PROGRAM INFORMATION: Prof. Thomas S. Wallsten Prof. Jonathan A. Marshall 919-962-2538, fax 919-962-2537 919-962-1887, fax 919-962-1799 E-mail: tom.wallsten@unc.edu E-mail: marshall@cs.unc.edu INFORMATION about registration, accommodations, and logistics: SMP Annual Meeting, Division of Continuing Education CB #1020, The Friday Center, University of North Carolina Chapel Hill, NC 27599-1020, USA. Phone: 919-962-2643 or 800-845-8640. Fax: 919-962-2061. E-mail: smp96@cs.unc.edu ---------------------------------------------------------------------------- 29th Annual Meeting of the Society for Mathematical Psychology 1-4 August 1996 and Workshop on Games and Behavior in honor of Amnon Rapoport 4-5 August 1996 REGISTRATION FORM Please type or print. One registration per form; duplicate as necessary. Name: Dr./Mr./Ms. ___________________________________________________________ last first m.i. Name as it should appear on name badge: ______________________________________ Social Security Number: ______________________________________________________ (optional; used for record keeping only) Affiliation: _________________________________________________________________ Address: _____________________________________________________________________ _____________________________________________________________________ City: ___________________________________ State: _______ Zip/Postal Code: __________________ Country: __________________ Phone: (_______) _____________________ Fax: (_______) _______________________ E-mail address: ______________________________________________________________ Special Needs (Individuals with special needs to accommodate a motor or sensory impairment should indicate their needs in the space below. Special dietary requirements should also be noted.): ______________________________________________________________________________ ______________________________________________________________________________ (continued) REGISTRATION FEES ( ) SMP Conference (includes Friday banquet) SMP Member ( ) By 22 July: $90 ( ) After 22 July: $105 Non-member ( ) By 22 July: $95 ( ) After 22 July: $110 Student ( ) By 22 July: $45 ( ) After 22 July: $ 60 ( ) Games and Behavior Workshop (registration does not include Sunday dinner) Regular Registration ( ) By 22 July: $35 ( ) After 22 July: $ 50 Registration with Proceedings ( ) By 22 July: $142 ( ) After 22 July: $157 Special fee for workshop participants ( ) SMP Saturday sessions: $26 ( ) SMP Sunday sessions: $13 Sunday Dinner ( ) Workshop participant: $25 ( ) Guest Meals ____ Friday banquet: $35 ____ Sunday dinner: $25 RESIDENCE HALL ACCOMMODATIONS (reservation and payment MUST be received by 15 July) ( ) Single: $37 / night ( ) Double: $22 per person / night (Roommate: __________________________) Arrival day, date, and time: ______________________________________________ Departure day, date, and time: ____________________________________________ PAYMENT Total Amount Due: $ ________________ ( ) Check or money order, made payable to Division of Continuing Education (Federal ID #56-6001393) ( ) Charge to credit card number: ____________________________________________ ( ) VISA ( ) MasterCard Expiration date: _____________________ Cardholder name: __________________ Signature: ________________________ ( ) Purchase order (P.O. number _____________________________________________) MAIL completed form and payment (US funds only) to: SMP Annual Meeting, Division of Continuing Education CB# 1020, The Friday Center, University of North Carolina Chapel Hill, NC 27599-1020, USA With credit card or purchase order information, registrations may be sent by Fax, 919-962-2061, or e-mail, smp96@cs.unc.edu. For security reasons, we do not recommend sending credit card numbers by e-mail; those choosing to register by e-mail may telephone to provide the credit card information. Registrations received after 22 July will be subject to a late fee of $15. ------------------------------------------------------------------------------ From Dimitris.Dracopoulos@trurl.brunel.ac.uk Fri Jun 14 17:50:51 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id RAA27852 for ; Fri, 14 Jun 1996 17:50:28 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id RAA20177 for ; Fri, 14 Jun 1996 17:50:26 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa03190; 14 Jun 96 17:39:16 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa03188; 14 Jun 96 17:18:30 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa27963; 14 Jun 96 17:17:56 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa20808; 14 Jun 96 9:23:16 EDT Received: from ceres.brunel.ac.uk by RI.CMU.EDU id aa24749; 14 Jun 96 9:22:15 EDT Received: from trurl.brunel.ac.uk by ceres.brunel.ac.uk with SMTP (PP); Fri, 14 Jun 1996 14:19:40 +0100 Received: by trurl.brunel.ac.uk (940816.SGI.8.6.9/940406.SGI.AUTO) id OAA01651; Fri, 14 Jun 1996 14:19:18 +0200 From: Dimitris Dracopoulos Message-Id: <9606141419.ZM1649@trurl.brunel.ac.uk> Date: Fri, 14 Jun 1996 14:19:16 -0600 Reply-To: Dimitris.Dracopoulos@brunel.ac.uk X-Mailer: Z-Mail (3.2.1 6apr95 MediaMail) To: Connectionists@cs.cmu.edu, GA-List@AIC.NRL.NAVY.MIL, cnes@brunel.ac.uk, genetic-programming@cs.stanford.edu Subject: Advanced MSc in Neural & Evolutionary Systems (London) Cc: Dimitris.Dracopoulos@brunel.ac.uk Mime-Version: 1.0 Content-Type: text/plain; charset=us-ascii The Department of Computer Science, Brunel University, London will run a new advanced MSc in Neural & Evolutionary Systems in 1996/1997. The MSc covers introductory and more advanced material in the areas of neural networks, genetic algorithms, genetic programming, artificial life, parallel computing, etc. The MSc in Neural and Evolutionary Systems is supported by the Centre of Neural and Evolutionary Systems (CNES), in the Department of Computer Science and Information Systems. More information about this MSc can be found in: http://http2.brunel.ac.uk:8080/~csstdcd/NES_Msc.html Applications will be considered until late June (or the latest beginning of July). For application forms please contact: Admissions Secretary Department of Computer Science and Information Systems Brunel University London Uxbridge Middlesex UB8 3PH United Kingdom Telephone: +44 (0)1895 274000 ext. 2394 Fax: +44 (0)1895 251686 Email: cs-msc-courses@brunel.ac.uk -- Dr Dimitris C. Dracopoulos Department of Computer Science Brunel University Telephone: +44 1895 274000 ext. 2120 London Fax: +44 1895 251686 Uxbridge E-mail: Dimitris.Dracopoulos@brunel.ac.uk Middlesex UB8 3PH United Kingdom From pkso@tattoo.ed.ac.uk Fri Jun 14 23:19:36 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id XAA00981 for ; Fri, 14 Jun 1996 23:19:30 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id XAA22557 for ; Fri, 14 Jun 1996 23:19:28 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa03199; 14 Jun 96 17:48:08 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab03188; 14 Jun 96 17:18:48 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa27968; 14 Jun 96 17:18:10 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa21792; 14 Jun 96 11:02:28 EDT Received: from haymarket.ed.ac.uk by RI.CMU.EDU id aa25286; 14 Jun 96 11:02:11 EDT Received: from tattoo.ed.ac.uk (tattoo.ed.ac.uk [129.215.128.24]) by haymarket.ed.ac.uk (8.6.13/8.6.12) with SMTP id QAA15958 for ; Fri, 14 Jun 1996 16:01:18 +0100 From: P Sollich Subject: Paper: Query learning in committee machine To: connectionists@cs.cmu.edu Date: Fri, 14 Jun 96 16:00:21 BST Message-ID: <9606141600.aa05180@uk.ac.ed.tattoo> FTP-host: archive.cis.ohio-state.edu FTP-filename: /pub/neuroprose/sollich.queries_comm_machine.ps.Z Dear connectionists, the following preprint is now available for copying from the neuroprose repository: Learning from Minimum Entropy Queries in a Large Committee Machine Peter Sollich Department of Physics University of Edinburgh Edinburgh EH9 3JZ, U.K. ABSTRACT In supervised learning, the redundancy contained in random examples can be avoided by learning from queries. Using statistical mechanics, we study learning from minimum entropy queries in a large tree-committee machine. The generalization error decreases exponentially with the number of training examples, providing a significant improvement over the algebraic decay for random examples. The connection between entropy and generalization error in multi-layer networks is discussed, and a computationally cheap algorithm for constructing queries is suggested and analysed. Has appeared in Physical Review E, 53, R2060--R2063, 1996 (4 pages). Comments and/or feedback are welcome. Peter Sollich PS: Sorry - no hardcopies available. -------------------------------------------------------------------------- Peter Sollich Department of Physics University of Edinburgh e-mail: P.Sollich@ed.ac.uk Kings Buildings phone: +44 - (0)131 - 650 5293 Mayfield Road Edinburgh EH9 3JZ, U.K. --------------------------------------------------------------------------