From terry@salk.edu Sun Aug 20 05:26:46 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Sun, 20 Aug 95 05:26:42 -0500; AA15737 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sun, 20 Aug 95 05:26:40 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa15710; 19 Aug 95 19:22:10 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa15708; 19 Aug 95 19:03:33 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa00973; 19 Aug 95 19:02:50 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa00977; 19 Aug 95 15:05:06 EDT Received: from helmholtz.salk.edu by CS.CMU.EDU id aa21408; 19 Aug 95 15:04:54 EDT Received: by salk.edu (4.1/SMI-4.1) id AA10190; Sat, 19 Aug 95 12:04:51 PDT Date: Sat, 19 Aug 95 12:04:51 PDT From: Terry Sejnowski Message-Id: <9508191904.AA10190@salk.edu> To: connectionists@cs.cmu.edu Subject: Neural Computation 7:5 Cc: terry@salk.edu NEURAL COMPUTATION September 1995 Volume 7 Number 5 Review: Methods for Combining Experts' Probability Assessments Robert A. Jacobs Letters: The Helmholtz Machine Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, and Richard S. Zemel Spontaneous Excitations in the Visual Cortex: Stripes, Spirals, Rings and Collective Bursts Corinna Fohlmeister, Wulfram Gerstner, Raphael Ritz, and J. Leo van Hemmen Time-Domain Solutions of Oja's Equations J. L. Wyatt, Jr. and I. M. Elfadel Learning the Initial State of a Second-Order Recurrent Neural Network during Regular-Language Inference Mikel L. Forcada and Rafael C. Carrasco An Algebraic Framework to Represent Finite State Machines in Single-Layer Recurrent Neural Networks R. Alquezar and A. Sanfeliu Local And Global Optimization Algorithms For Generalized Learning Automata V. V. Phansalkar and M. A. L. Thathachar >From Data Distributions to Regularization in Invariant Learning Todd K. Leen Initializing Weights of a Multilayer Perceptron Network by Using the Orthogonal Least Squares Algorithm Mikko Lehtokangas, Jukka Saarinen, Pentti Huuhtanen and Kimmo Kaski Learning and Generalization in Radial Basis Function Networks J. A. S. Freeman and D. Saad Precision and Approximate Flatness in Artificial Neural Networks Maxwell Stinchcombe Lower Bounds on the VC Dimension of Smoothly Parametrized Function Classes Wee Sun Lee, Peter L. Bartlett and Robert C. Williamson Agnostic PAC Learning of Functions on Analog Neural Nets Wolfgang Maass Convex Potentials and their Conjugates in Analog Mean-Field Optimization I. M. Elfadel Patterns of Functional Damage in Neural Network Models of Associative Memory Eytan Ruppin and James A. Reggia ----- ABSTRACTS - http://www-mitpress.mit.edu/ SUBSCRIPTIONS - 1995 - VOLUME 7 - BIMONTHLY (6 issues) ______ $40 Student and Retired ______ $68 Individual ______ $180 Institution Add $22 for postage and handling outside USA (+7% GST for Canada). (Back issues from Volumes 1-6 are regularly available for $28 each to institutions and $14 each for individuals Add $5 for postage per issue outside USA (+7% GST for Canada) MIT Press Journals, 55 Hayward Street, Cambridge, MA 02142. Tel: (617) 253-2889 FAX: (617) 258-6779 e-mail: hiscox@mitvma.mit.edu ----- From lbl@nagoya.bmc.riken.go.jp Sun Aug 20 17:13:53 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Sun, 20 Aug 95 17:13:51 -0500; AA19436 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sun, 20 Aug 95 17:13:49 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa16371; 20 Aug 95 12:38:30 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa16369; 20 Aug 95 12:26:45 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa01733; 20 Aug 95 12:26:17 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa05203; 20 Aug 95 4:51:21 EDT Received: from ns.bmc.riken.go.jp by CS.CMU.EDU id aa24849; 20 Aug 95 4:51:09 EDT Received: from xian (xian [134.160.96.17]) by nagoya.bmc.riken.go.jp (8.6.12+2.5Wb7/3.4Wbeta6-BMC-95061514) with SMTP id MAA17310; Sun, 20 Aug 1995 12:27:41 +0900 From: Bao-Liang Lu Received: by xian (5.x/3.4Wbeta6) id AA08838; Sun, 20 Aug 1995 12:32:26 +0900 Date: Sun, 20 Aug 1995 12:32:26 +0900 Message-Id: <9508200332.AA08838@xian> To: Connectionists@cs.cmu.edu Subject: Paper available:Inverse Kinematics via Network Inversions Cc: lbl@nagoya.bmc.riken.go.jp X-Sun-Charset: US-ASCII The following paper, to appear in Proc. of IEEE ICNN'95, Perth, Australia, is available via FTP. FTP-host:ftp.bmc.riken.go.jp FTP-file:pub/publish/Lu/lu-ieee-icnn95.ps.Z ========================================================================== TITLE: Regularization of Inverse Kinematics for Redundant Manipulators Using Neural Network Inversions AUTHORS: Bao-Liang Lu (1) Koji Ito (1,2) ORGANISATIONS: (1) The Institute of Physical and Chemical Research (2) Toyohashi University of Technology ABSTRACT: This paper presents a new approach to regularizing the inverse kinematics problem for redundant manipulators using neural network inversions. This approach is a four-phase procedure. In the first phase, the configuration space and associated workspace are partitioned into a set of regions. In the second phase, a set of modular neural networks is trained on associated training data sets sampled over these regions to learn the forward kinematic function. In the third phase, the multiple inverse kinematic solutions for a desired end-effector position are obtained by inverting the corresponding modular neural networks. In the fourth phase, an ``optimal" inverse kinematic solution is selected from the multiple solutions according to a given criterion. This approach has an important feature in comparison with existing methods, that is, both the inverse kinematic solutions located in the multiple solution branches and the ones that belong to the same solution branch can be found. As a result, better control of the manipulator using the optimum solution than that using an ordinary solution can be achieved. This approach is illustrated with a three-joint planar arm. (6 pages. No hard copies available.) Bao-Liang Lu --------------------------------------------- Bio-Mimetic Control Research Center, The Institute of Physical and Chemical Research (RIKEN) 3-8-31 Rokuban, Atsuta-ku, Nagoya 456, Japan Phone: +81-52-654-9137 Fax: +81-52-654-9138 Email: lbl@nagoya.bmc.riken.go.jp From M.Q.Brown@ecs.soton.ac.uk Mon Aug 21 18:38:51 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 21 Aug 95 18:38:49 -0500; AA01398 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Mon, 21 Aug 95 18:38:47 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa18206; 21 Aug 95 17:22:46 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa18204; 21 Aug 95 17:08:17 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa03100; 21 Aug 95 17:08:11 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa19535; 21 Aug 95 12:12:46 EDT Received: from beech.soton.ac.uk by EDRC.CMU.EDU id aa22075; 21 Aug 95 12:12:00 EDT Received: from bright.ecs.soton.ac.uk (bright.ecs.soton.ac.uk [152.78.64.201]) by beech.soton.ac.uk (8.6.12/hub-8.5a) with SMTP id RAA26991 for ; Mon, 21 Aug 1995 17:05:30 +0100 Received: from diana.ecs.soton.ac.uk by bright.ecs.soton.ac.uk; Mon, 21 Aug 95 17:04:35 BST Date: Mon, 21 Aug 1995 17:05:09 +0100 Message-Id: <17225.199508211605@diana.ecs.soton.ac.uk> X-Sender: mqb@diana X-Mailer: Windows Eudora Version 1.4.3 Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" To: Connectionists@cs.cmu.edu From: martin brown Subject: ISIS web entry The Image, Speech and Intelligent Systems (ISIS) research group now has an entry at: http://www-isis.ecs.soton.ac.uk/ We've been doing research into many different aspects of neural and neurofuzzy theory for the past 6 years, especially applied to classification and identification and control theory. A lot of work has been done on neurofuzzy networks and the development of automatic construction algorithms. These pages include: 1) Publication details together with abstracts. 2) Project details. 3) Personnel details. 4) Neurofuzzy information service (conferences, software, other homepages etc.) Martin Brown ------------------------------------------------------ ISIS research group, Email: mqb@ecs.soton.ac.uk Room 3025, Zepler Building, Tel: +44 (0)1703 594984 Dept. of Electronics and Computer Science, Fax: +44 (0)1703 594498 University of Southampton, WWW: http://www-isis.ecs.soton.ac.uk/ Highfield, Southampton, SO17 1BJ, UK From charles@playfair.Stanford.EDU Tue Aug 22 20:20:01 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 22 Aug 95 20:19:59 -0500; AA12251 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 22 Aug 95 20:19:57 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa19395; 22 Aug 95 20:09:44 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa19391; 22 Aug 95 19:50:14 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04280; 22 Aug 95 19:49:46 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa07253; 22 Aug 95 13:48:33 EDT Received: from Playfair.Stanford.EDU by CS.CMU.EDU id aa14711; 22 Aug 95 13:47:10 EDT Received: from tukey.Stanford.EDU (tukey [36.63.0.11]) by playfair.Stanford.EDU (8.6.10/8.6.10) with ESMTP id KAA10396 for ; Tue, 22 Aug 1995 10:47:07 -0700 Received: from localhost (charles@localhost) by tukey.Stanford.EDU (8.6.10/8.6.10) with SMTP id KAA06364 for ; Tue, 22 Aug 1995 10:47:06 -0700 Message-Id: <199508221747.KAA06364@tukey.Stanford.EDU> X-Authentication-Warning: tukey.Stanford.EDU: charles owned process doing -bs X-Authentication-Warning: tukey.Stanford.EDU: Host localhost didn't use HELO protocol To: connectionists@cs.cmu.edu Subject: Paper available: Visualization of High-Dimensional Functions Date: Tue, 22 Aug 95 10:47:05 -0700 From: charles@playfair.Stanford.EDU The following doctoral dissertation (168 pages) is now available electronically: ftp://playfair.stanford.edu/pub/roosen/thesis.ps.Z A short (6 page) proceedings paper on the same material is also available: ftp://playfair.stanford.edu/pub/roosen/asa95.ps.Z Charles Roosen charles@playfair.stanford.edu Department of Statistics http://playfair.stanford.edu/~roosen/ Stanford University Title ----- Visualization and Exploration of High-Dimensional Functions Using the Functional ANOVA Decomposition Abstract -------- In recent years the statistical and engineering communities have developed many high-dimensional methods for regression (e.g.\ MARS, feedforward neural networks, projection pursuit). Users of these methods often wish to explore how particular predictors affect the response. One way to do so is by decomposing the model into low-order components through a functional ANOVA decomposition and then visualizing the components. Such a decomposition, with the corresponding variance decomposition, also provides information on the importance of each predictor to the model, the importance of interactions, and the degree to which the model may be represented by first and second-order components. This manuscript develops techniques for constructing and exploring such a decomposition. It begins by suggesting techniques for constructing the decomposition either numerically or analytically, proceeds to describe approaches to plotting and interpreting effects, and then develops methodology for rough inference and model selection. Extensions to the GLM framework are discussed briefly. From mw@isds.Duke.EDU Wed Aug 23 01:37:46 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 23 Aug 95 01:37:44 -0500; AA14215 Message-Id: <9508230637.AA15807@lucy.cs.wisc.edu> Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 23 Aug 95 01:37:42 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa19420; 22 Aug 95 20:16:44 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa19397; 22 Aug 95 19:51:06 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04286; 22 Aug 95 19:50:36 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa09350; 22 Aug 95 15:31:02 EDT Received: from serov.isds.duke.edu by RI.CMU.EDU id aa28088; 22 Aug 95 15:30:57 EDT Received: from bosco.isds.duke.edu [152.3.22.42] by serov.isds.duke.edu Ver 8.6.10 ESMTP id PAA20588; Tue, 22 Aug 1995 15:31:12 -0400 From: Mike West Date: Tue, 22 Aug 1995 15:30:51 -0400 To: connectionists@cs.cmu.edu 1996 JOINT STATISTICAL MEETINGS Chicago, August 4-8, 1996 ********************************************** ASA Section on BAYESIAN STATISTICAL SCIENCES ********************************************** CALL FOR PROPOSALS: SPECIAL CONTRIBUTED PAPER SESSIONS This is an initial call for proposals for Special Contributed Paper Sessions for the Section on Bayesian Statistical Sciences program at the 1996 Joint Meetings. We already have several proposals, at least in embryo, and are looking for many more. Please consider putting together a session, and alert others who may be interested in organising a session. We have a few months before formal sessions, tentative titles, etc., are needed, but it is not too soon to begin to think about session themes and to talk to potential participants. Those of you who have already contacted me with ideas should now be going the next step to confirm your topics and speakers, and let me have the revised session proposal. At this stage, suggestions and ideas for sessions need not identify a full list of speakers and discussants, but you should provide a general idea of the topic and focus, and some names at least tentatively agreed. The ASA theme for the 1996 meetings is "Challenging the Frontiers of Knowledge Using Statistical Science" and is intended to highlight new statistical developments at the forefront of the discipline -- theory, methods, applications, and cross-disciplinary activities. Accordingly, ASA will be designating selected sessions as "Theme" sessions. Suggestions for SBSS sessions obviously in tune with this theme, involving topics of real novelty and importance, new directions of development in Bayesian statistics, and reflecting the current vibrancy of the discipline, are particularly encouraged. And remember that other ASA sections can co-sponsor sessions; SBSS will be vigorously seeking co-sponsorship in many cases. *SPECIAL REQUEST* The Chicago meetings will likely attract well over 4,000 participants. Many will not be seriously attracted by many of the Bayesian talks; not because of Bayesian emphases, but because of technical/mathematical level and orientation. For example, there will be many participants from various corners of industry/commerce/government that do not have PhDs. We would like to reach these kinds of people. One way of attempting this is to have one or more sessions more or less targeted at the practising "applied" statistician and at a lower technical level than typical. One possibility is a panel discussion, involving participants with strong industrial/consulting/non-academic backgrounds who will discuss issues and experiences of practical Bayesian statistics in their areas. A variant would have several speakers making "case study" presentations. Your inputs are solicited. FORMAT: A Special Contributed Paper Session typically involves a collections of either five short talks (20mins), or four talks plus a discussion, on a specific theme. One alternative is to have a panel discussion involving up to five panelists. All sessions need a chair. Please contact me with suggestions and ideas for themes and speakers. Mike West, 1996 SBSS Program Chair ISDS, Duke University, Durham, NC 27708-0251 mw@isds.duke.edu http://www.isds.duke.edu From hochreit@informatik.tu-muenchen.de Wed Aug 23 12:02:36 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 23 Aug 95 12:02:34 -0500; AA20869 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 23 Aug 95 12:02:32 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa20361; 23 Aug 95 11:17:55 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa20359; 23 Aug 95 11:09:17 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa05261; 23 Aug 95 11:08:41 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa19144; 23 Aug 95 5:16:41 EDT Received: from papa.informatik.tu-muenchen.de by RI.CMU.EDU id aa00484; 23 Aug 95 5:15:42 EDT Received: from gnom.informatik.tu-muenchen.de by papa.informatik.tu-muenchen.de id <116236>; Wed, 23 Aug 1995 11:15:12 +0200 From: Josef Hochreiter To: connectionists@cs.cmu.edu Subject: TR announcement: Long Short Term Memory Message-Id: <95Aug23.111512+0200_met_dst.116236+322@papa.informatik.tu-muenchen.de> Date: Wed, 23 Aug 1995 11:15:10 +0200 FTP-host: flop.informatik.tu-muenchen.de (131.159.8.35) FTP-filename: /pub/fki/fki-207-95.ps.gz or FTP-host: fava.idsia.ch (192.132.252.1) FTP-filename: /pub/juergen/fki-207-95.ps.gz or something like netscape http://www.idsia.ch/~juergen LONG SHORT TERM MEMORY Technical Report FKI-207-95 (8 pages, 50 K) Sepp Hochreiter Juergen Schmidhuber Fakultaet fuer Informatik IDSIA Technische Universitaet Muenchen Corso Elvezia 36 80290 Muenchen, Germany 6900 Lugano, Switzerland ``Recurrent backprop'' for learning to store information over extended time periods takes too long. The main reason is insufficient, decaying error back flow. We describe a novel, efficient ``Long Short Term Memory'' (LSTM) that overcomes this and related problems. Unlike previous approaches, LSTM can learn to bridge arbitrary time lags by enforcing constant error flow. Using gradient descent, LSTM explicitly learns when to store information and when to access it. In experimental comparisons with ``Real-Time Recurrent Learning'', ``Recurrent Cascade-Correlation'', ``Elman nets'', and ``Neural Sequence Chunking'', LSTM leads to many more successful runs, and learns much faster. Unlike its competitors, LSTM can solve tasks involving minimal time lags of more than 1000 time steps, even in noisy environments. If you don't have gzip/gunzip, we can mail you an uncompressed postscript version (as a last resort). Comments welcome. Sepp Hochreiter Juergen Schmidhuber From Voz@dice.ucl.ac.be Wed Aug 23 18:14:35 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 23 Aug 95 18:14:30 -0500; AA24008 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 23 Aug 95 18:14:27 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa20534; 23 Aug 95 15:17:47 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa20532; 23 Aug 95 15:09:55 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa05399; 23 Aug 95 15:09:26 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa26702; 23 Aug 95 12:57:09 EDT Received: from ns1.dice.ucl.ac.be by CS.CMU.EDU id aa24593; 23 Aug 95 12:56:45 EDT Received: by ns1.dice.ucl.ac.be (mp-14/10/92/SMI-4.1) id SAA11205; Wed, 23 Aug 1995 18:58:09 +0200 Date: Wed, 23 Aug 1995 18:58:09 +0200 From: Jean-Luc Voz Message-Id: <199508231658.SAA11205@ns1.dice.ucl.ac.be> To: CLASS-L@CCVM.SUNYSB.EDU, Connectionists@cs.cmu.edu Subject: ELENA Classification databases and technical reports available Dear colleagues, The partners of the Elena project are pleased to announce you the availability of several databases related to classification together with two technical reports. ELENA is an ESPRIT III Basic Research Action project (No. 6891) >From July 92 to June 95 the ELENA project investigated several aspects of classification by neural networks, including links between neural networks and Bayesian statistical classification, incremental learning,... The project includes theoretical work on classification algorithms, simulations and benchmarks, especially on realistic industrial data. Hardware implementation, especially VLSI option, is the last objective. The set of databases available is to be used for tests and benchmarks of machine-learning classification algorithms. The databases are splitted into two parts: ARTIFICIALly generated databases, mainly used for preliminary tests, and REAL ones, used for objective benchmarks and comparisons of methods. The choice of the databases has been guided by various parameters, such as availability of published results concerning conventional classification algorithms, size of the database, number of attributes, number of classes, overlapping between classes and non-linearities of the borders,... Results of PCA and DFA preprocessing of the REAL databases are also included, together with several measures useful for the databases characterization (statistics, fractal dimension, dispersion,...). All these databases and their preprocessing are available together with a postcript technical report describing in details the different databases ('Databases.ps.Z' - 45 pages - 777781 bytes) and a report related to the comparative benchmarking studies of various algorithms ('Benchmarks.ps.Z' - 113 pages - 1927571 bytes) well-known by the Statistical and Neural Network communities (MLP, RCE, LVQ, k_NN, GQC) or developped in the framework of the Elena project (IRVQ, PLS). A LaTeX bibfile containing more than 90 entries corresponding to the Elena partners bibliography related to the project is also available ('Elena.bib') in the same directory. All files are available by anonymous ftp from the following directory: ftp://ftp.dice.ucl.ac.be/pub/neural-nets/ELENA/databases The databases are splitted into two parts: the 'ARTIFICIAL' ones, being generated in order to obtain some defined characteristics, and for which the theoretical Bayes error can be computed, and the 'REAL' ones, collected in existing real-world applications. The ARTIFICIAL databases ('Gaussian', 'Clouds' and 'Concentric') were generated according to the following requirements: - heavy intersection of the class distributions, - high degree of nonlinearity of the class boundaries, - various dimensions of the vectors, - already published results on these databases. They are restricted to two-class problems, since we believe it yield answers to the most essential questions. The ARTIFICIAL databases are mainly used for rapid test purposes on newly developed algorithms. The REAL databases ('Satimage', 'Texture', 'Iris' and 'Phoneme') were selected according to the following requirements: - classical databases in the field of classification (Iris), - already published results on these databases (Phoneme, from the ROARS ESPRIT project and 'Satimage' from the STATLOG ESPRIT project), - various dimensions of the vectors, - sufficient number of vectors (to avoid the ``empty space phenomenon''). - the 'Texture' database, generated at INPG for the Elena project is interesting for its high number of classes (11). ############################################################################## ########### # DETAILS # ########### The 'Benchmarks' technical report ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The 'Benchmarks.ps' Elena report is related to the benchmarking studies of various classifiers. Most of the classifiers which were used for the benchmark comparative studies are are well known by the neural network and machine learning community. These are the k-Nearest Neighbour (k_NN) classifier, selected for its powerful probability density estimation properties; the Gaussian Quadratic Classifier (GQC), the most classical statistical parametric simple classification method; the Learning Vector Quantizer (LVQ), a powerful non-linear iterative learning algorithm proposed by Kohonen; the Reduced Coulomb Energy (RCE) algorithm, an incremental Region Of Influence algorithm; the Inertia Rated Vector Quantizer (IRVQ) and the Piecewise Linear Separation (PLS) classifiers, developed in the framework of the Elena project. The main objectives of the 'Benchmarks.ps' Elena report report are the following: - to provide an overall comprehensive view of the general problem of comparative benchmarking studies and to propose a useful common test basis for existing and further classification methods, - to obtain objective comparisons of the different chosen classifiers on the set of databases described in this report (each classifier being used with its optimal configuration for each particular database), - to study the possible links between the data structures of the databases viewed by some parameters, and the behavior of the studied classifiers (mainly the evolution of their the optimal configuration parameters). - to study the links between the preprocessing methods and the classification algorithms from the performances and hardware constraints point of view (especially the computation times and memory requirements). Databases format ~~~~~~~~~~~~~~~~ All the databases available are in the following format (after decompression) : - All files containing the databases are stored as ASCII files for their easy edition and checking. - In a file, each of the n lines is reserved for each vectorial sample (instance) and each line consists of d floating-point numbers (the attributes) followed by the class label (which must be an integer). Example: 1.51768 12.65 3.56 1.30 73.08 0.61 8.69 0.00 0.14 1 1.51747 12.84 3.50 1.14 73.27 0.56 8.55 0.00 0.00 0 1.51775 12.85 3.48 1.23 72.97 0.61 8.56 0.09 0.22 1 1.51753 12.57 3.47 1.38 73.39 0.60 8.55 0.00 0.06 1 1.51783 12.69 3.54 1.34 72.95 0.57 8.75 0.00 0.00 3 1.51567 13.29 3.45 1.21 72.74 0.56 8.57 0.00 0.00 1 There are NO missing values. If you desire to get a database, you MUST do it in ftp the binary mode. So if you aren't in this mode, simply type 'binary' at the ftp prompt. EXAMPLE: to get the "phoneme" database : cd REAL cd phoneme binary get phoneme.txt get phoneme.dat.Z get ... cd ... ... quit After your ftp session, you simply have to type 'uncompress phoneme.dat.Z' to get the uncompressed datafile. Contents of the 'ARTIFICIAL' directory ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The databases of this directory contain only the 'ARTIFICIAL' classification problems. The present 'ARTIFICIAL' databases are only two-class problems, since it yields answers to the most essential questions. For each problem, the confusion matrix corresponding to the theoretical Bayes boundary is provided with the confusion matrix obtained by a k_NN classifier (k chosen to reach the minimum of the total Leave-One-Out error). These databases were selected to use for preliminary test and to study the behavior of the implemented algorithms for some particular problems: - Overlapping classes: The classifier should have the ability to form a decision boundary that minimizes the amount of misclassification for all of the overlapping classes. - Nonlinear separability: The classifier should be able to build decision regions that separate classes of any shape and size. There is one subdirectory for each database. In this subdirectory, there is : - A text file providing detailed information about the related database ('databasename.txt'). - The compressed database ('databasename.dat.Z). The different patterns of each database are presented in a random order. - For bidimensional databases, a postscript file representing the 2-D datasets (those files are in eps format). For each subdirectory, the directoryname is the same as the name chosen for the concerned database. Here are the directorynames with a brief description. - 'clouds' Bidimensional distributions : the class 0 is the sum of three different normal distributions while the the class 1 is another normal, overlapping the class 0. 5000 patterns, 2500 in each class. This allows the study of the classifier behavior for heavy intersection of the class distributions and for high degree of nonlinearity of the class boundaries. - 'gaussian' A set of seven databases corresponding to the same problem, but with dimensionality ranging from 2 to 8. This allows the study of the classifier behavior for different dimensionalities of the input vectors, for heavy overlapped distributions and for non linear separability. Theses databases where already studied by Kohonen in: Kohonen, T. and Barna, G. and Chrisley, R., "Statistical Pattern Recognition with Neural Networks: Benchmarking Studies", IEEE Int. Conf. on Neural Networks, SOS Printing, San Diego, 1988. In this paper,the performances of three basis types of neural-like networks (Backpropagation network, Boltzmann machine and Learning Vector Quantization) is evaluated and compared to the theoretical limit. - 'concentric' Bidimensional uniform concentric circular distributions. 2500 instances, 1579 in class 1, 921 in class 0. This database may be used to study the linear separability of the classifier when some classes are nested in other without overlapping. Contents of the 'REAL' directory ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The databases of this directory contain only the real classification problem sets selected for the Elena benchmarking studies. There is one subdirectory for each database. In this subdirectory, there are: - a text file giving detailed information about the related database (`databasename.txt'), - the compressed original database in the Elena format (`databasename.dat.Z'); the different patterns of each database being presented in a random order. - By the way of a normalization process, each original feature will have the same importance in a subsequent classification process. A typical method is first to center each feature separately and than to reduce it to a unit variance; this process has been applied on all the REAL Elena databases in order to build the ``CR'' databases contained in the ``databasename_CR.dat.Z'' files. The Principal Components Analysis (PCA) is a very classical method in pattern recognition [Duda73]. PCA reduces the sample dimension in a linear way for the best representation in lower dimensions keeping the maximum of inertia. The best axe for the representation is however not necessary the best axe for the discrimination. After PCA, features are selected according to the percentage of initial inertia which is covered by the different axes and the number of features is determined according to the percentage of initial inertia to keep for the classification process. This selection method has been applied on every REAL database after centering and reduction (thus on the databasename_CR.dat files). When quasi-linear correlations exists between some initial features, these redundant dimensions are removed by PCA and this preprocessing is then recommended. In this case, before a PCA, the determinant of the data covariance matrix is near zero; this database is thus badly conditioned for all process which use this information (the quadratic classifier for example). The following files, related to PCA are also available for the REAL databases: - ``databasename_PCA.dat.Z'', the projection of the ``CR'' database on its principal components (sorted in a decreasing order of the related inertia percentage), - ``databasename_corr_circle.ps.Z'', a graphical representation of the correlation between the initial attributes and the two first principal components, - ``databasename_proj_PCA.ps.Z'', a graphical representation of the projection of the initial database on the two first principal components, - ``databasename_EV.dat'', a file with the eigenvalues and associated inertia percentages The Discriminant Factorial Analysis (DFA) can be applied to a learning database where each learning sample belongs to a particular class [Duda73]. The number of discriminant features selected by DFA is fixed in function of the number of classes (c) and of the number of input dimensions (d); this number is equal to the minimum between d and c-1. In the usual case where d is greater than c, the output dimension is fixed equal to the number of classes minus one and the discriminant axes are selected in order to maximize the between-variance and to minimize the within-variance of the classes. The discrimination power (ratio of the projected between-variance over the projected within-variance) is not the same for each discriminant axis: this ratio decreases for each axis. So for a problem with many classes, this preprocessing will not be always efficient as the last output features will not be so discriminant. This analysis uses the information of the inverse of the global covariance matrix, so the covariance matrix must be well conditioned (for example, a preliminary PCA must be applied to remove the linearly correlated dimensions). The DFA preprocessing method has been applied on the 18 first principal components of the 'satimage_PCA' and 'texture_PCA' databases (thus by keeping only the 18 first attributes of these databases before to apply the DFA preprocessing) in order to build the 'satimage_DFA.dat.Z' and 'texture_DFA.dat.Z' database files, having respectively 5 and 10 dimensions (the 'satimage' database having 6 classes and 'texture' 11). For each subdirectory, the directoryname is the same as the name chosen for the contained database. Here are the directorynames with a brief numerical description of the available databases. - phoneme French and Spannish phoneme recognition problem. The aim is to distinguish between nasal (AN, IN, ON) and oral (A, I, O, E, E') vowels. 5404 patterns, 5 attributes (the normalized amplitudes of the five first harmonics), 2 classes. This database was in use in the European ESPRIT 5516 project ROARS. The aim of this project is the development and the implementation of a REAL time analytical system for French and Spannish phoneme recognition. - texture The aim is to distinguish between 11 different textures (Grass lawn, Pressed calf leather, Handmade paper, Raffia looped to a high pile, Cotton canvas, ...), each pattern (pixel) being characterised by 40 attributes built by the estimation of fourth order modified moments in four orientations: 0, 45, 90 and 135 degrees. 5500 patterns, 11 classes of 500 instances (each class refers to a type of texture in the Brodatz album). The original source of this database is: P. Brodatz "Textures: A Photographic Album for Artists and Designers", Dover Publications, Inc., New York, 1966. This database was generated by the Laboratory of Image Processing and Pattern Recognition (INPG-LTIRF Grenoble, France) in the development of the Esprit project ELENA No. 6891 and the Esprit working group ATHOS No. 6620. - satimage (*) Classification of the multi-spectral values of an image of the Landsat satellite. Each line contains the pixel values in four spectral bands of each of the 9 pixels in a 3x3 neighbourhood and a number indicating the classification label of the central pixel (corresponding to the type of soil: red soil, cotton crop, grey soil, ...). The aim is to predict this classification, given the multi-spectral values. 6435 instances, 36 attributes (4 spectral bands x 9 pixels in neighbourhood), 6 classes. This database was in use in the European StatLog project, which involves comparing the performances of machine learning, statistical, and neural network algorithms on data sets from REAL-world industrial areas including medicine, finance, image analysis, and engineering design: D. Michie, D.J. Spiegelhalter, and C.C. Taylor, editors. Machine learning, Neural and Statistical Classification. Ellis Horwood Series In Artificial Intelligence, England, 1994. - iris (*) This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. 4 attributes (sepal length, sepal width, petal length and petal width). (*) These databases are taken from the ftp anonymous "UCI Repository Of Machine Learning Databases and Domain Theories" (ics.uci.edu: pub/machine-learning-databases): Murphy, P. M. and Aha, D. W. (1992). "UCI Repository of machine learning databases" [Machine-readable data repository]. Irvine, CA: University of California, Department of Information and Computer Science. [Duda73] Duda, R.O. and Hart, P.E., Pattern Classification and Scene Analysis, John Wiley & Sons, 1973. ############################################################################## The ELENA PROJECT ~~~~~~~~~~~~~~~~~ Neural networks are now known as powerful methods for empirical data analysis, especially for approximation (identification, control, prediction) and classification problems. The ELENA project investigates several aspects of classification by neural networks, including links between neural networks and Bayesian statistical classification, incremental learning (control of the network size by adding or removing neurons),... URL: http://www.dice.ucl.ac.be/neural-nets/ELENA/ELENA.html ELENA is an ESPRIT III Basic Research Action project (No. 6891). It involves: INPG (Grenoble, F), UPC (Barcelona, E), EPFL (Lausanne, CH), UCL (Louvain-la-Neuve, B), Thomson-Sintra ASM (Sophia Antipolis, F) EERIE (Nimes, F). The coordinator of the project can be contacted at: Prof. Christian Jutten, INPG-LTIRF, 46 av. Flix Viallet, F-38031 Grenoble Cedex, France Phone: +33 76 57 45 48, Fax: +33 76 57 47 90, e-mail: chris@tirf.inpg.fr A simulation environment (PACKLIB) has been developed in the project; it is a smart graphical tool allowing fast programming and interactive analysis. The PACKLIB environment greatly simplifies the user's task by requiring only to write the basic code of the algorithms, while the whole graphical input, output and relationship framework is handled by the environment itself. PACKLIB is used for extensive benchmarks in the ELENA project and in other situations (image processing, control of mobile robots,...). Currently, PACKLIB is tested by beta users and a demo version available in the public domain. URL: http://www.dice.ucl.ac.be/neural-nets/ELENA/Packlib.html ############################################################################## IF YOU HAVE ANY PROBLEM, QUESTION OR PROPOSITION, PLEASE E_MAIL the following. VOZ Jean-Luc or Michel Verleysen Universite Catholique de Louvain DICE - Lab. de Microelectronique 3, place du Levant B-1348 LOUVAIN-LA-NEUVE E_mail : voz@dice.ucl.ac.be verleysen@dice.ucl.ac.be From S.Renals@dcs.shef.ac.uk Wed Aug 23 18:14:36 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 23 Aug 95 18:14:32 -0500; AA24010 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 23 Aug 95 18:14:30 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa20546; 23 Aug 95 15:24:55 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa20539; 23 Aug 95 15:10:49 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa05407; 23 Aug 95 15:10:12 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa28367; 23 Aug 95 14:12:02 EDT Received: from gw.dcs.shef.ac.uk by CS.CMU.EDU id aa25430; 23 Aug 95 14:11:08 EDT Received: from elvis.dcs.shef.ac.uk by dcs.shef.ac.uk (4.1/DAVE-1.0) id AA10720; Wed, 23 Aug 95 19:10:58 BST Received: by elvis.dcs.shef.ac.uk (8.6.9/SMI-4.1) id TAA11146; Wed, 23 Aug 1995 19:11:06 +0100 Date: Wed, 23 Aug 1995 19:11:06 +0100 From: S.Renals@dcs.shef.ac.uk Message-Id: <199508231811.TAA11146@elvis.dcs.shef.ac.uk> To: ml-connectionists@cs.cmu.edu Cc: ajr@eng.cam.ac.uk, mmh@eng.cam.ac.uk, s.renals@dcs.shef.ac.uk Subject: AbbotDemo: Continuous Speech Recognition Available by FTP Reply-To: AbbotDemo@compute.demon.co.uk AbbotDemo is a near-real-time speaker-independent continuous speech recognition system for American and British accented English. The vocabulary size of this demonstration system is 5,000 words. The system uses a hybrid recurrent network/hidden Markov model acoustic model and a trigram language model. The recurrent network (which contains around 100,000 weights) was trained as a phone probability estimator using back-propagation through time. It is available by FTP from svr-ftp.eng.cam.ac.uk in directory /pub/comp.speech/binaries. The file AbbotDemo.README gives more information on this system and the remainder of the files provide the executables for various flavours of UNIX (Linux, SunOS4, HP-UX, IRIX). A 16 bit soundcard and a reasonable microphone are required. The Linux version is also available by FTP from sunsite.unc.edu (and mirrors) in directory /pub/Linux/apps/sound/speech. Sorry, but at this stage no sources and only limited documentation are provided. Although the task domain is focused on noise-free read speech from a north American business newspaper (e.g. the Wall Street Journal) we hope that this system provides a fair representation of the state of the art in large vocabulary speech recognition and that it will encourage the creation of novel applications. Tony Robinson (Cambridge University) Mike Hochberg (Cambridge University) Steve Renals (Sheffield University) and many many more. AbbotDemo URLs: ftp://svr-ftp.eng.cam.ac.uk/pub/comp.speech/binaries/ ftp://sunsite.unc.edu/pub/Linux/apps/sound/speech/ From wahba@stat.wisc.edu Thu Aug 24 06:39:08 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 24 Aug 95 06:39:06 -0500; AA28640 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 24 Aug 95 06:39:04 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa20874; 23 Aug 95 19:42:44 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa20870; 23 Aug 95 19:35:36 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa05648; 23 Aug 95 19:35:18 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa29801; 23 Aug 95 15:19:29 EDT Received: from hera.stat.wisc.edu by EDRC.CMU.EDU id aa02781; 23 Aug 95 15:19:03 EDT Date: Wed, 23 Aug 95 14:19:00 -0500 From: Grace Wahba Message-Id: <9508231919.AA12253@hera.stat.wisc.edu> Received: by hera.stat.wisc.edu; Wed, 23 Aug 95 14:19:00 -0500 To: connectionists@cs.cmu.edu Subject: spline/ai/met paper ancts Cc: wahba@stat.wisc.edu Announcing new/revised manuscripts available on the web .... http/www.stat.wisc.edu/~wahba click on `TRLIST' also available by ftp in files listed at the end of this msg Luo, Z. and Wahba, G. "Hybrid Adaptive Splines" TR 947, June 1995, submitted. --- Combines ideas from smoothing splines and MARS to obtain spatial adaptivity, numerical efficiency. Numerical comparisons to wavelet simulations in Donoho et al, JRSSB 57, 1995. Approach parallels Orr, Neural Computation 7,1995, which we just became aware of-similar results on a different class of examples --. Wahba, G., Wang, Y., Gu, C., Klein, R. and Klein, B. " Smoothing Spline ANOVA for Exponential Families, with Application to the Wisconsin Epidemiological Study of Diabetic Retinopathy." May 1995, to appear, Annals of Statistics. --Expanded and *slightly revised* version of TR 940, December 1994. This paper was the basis for the Neyman Lecture given at the Annual Meeting of the Institute of Mathematical Statistics at Chapel Hill 1994, delivered by the first author. Collects results from SS-ANOVA, algorithm for choosing multiple smoothing parameters for Bernoulli data, implementing confidence intervals. Applies results to the estimation of four- year risk of progression of diabetic retinopathy, using data from the Wisconsin Epidemiological Study of Diabetic Retinopathy. (data available in GRKPACK documentation above). ***A discussion of the backfitting algorithm and SS-ANOVA has been added***--. Xiang, D. and Wahba, G. " A Generalized Approximate Cross Validation for Smoothing Splines with Non-Gaussian Data." TR 930, September 1994, submitted. --Another look at choosing the regularization parameter for Bernoulli data. Parallels work of Moody, Liu in the NN literature. Wahba, G., Johnson, D. R., Gao, F. and Gong, J. " Adaptive tuning of numerical weather prediction models: Part I: randomized GCV and related methods in three and four dimensional data assimilation." TR 920, April 1994. *revised and shortened* version to appear, Monthly Weather Review --describes how to use the randomized trace method to implement GCV in very large problems in numerical weather prediction, approximate solution of pde's with discrete noisy observations. Also available by ftp in gzipped postscript in ftp.stat.wisc.edu/pub/wahba in the following files, respectively has.ps.gz exptl.ssanova.rev.ps.gz gacv.ps.gz tuning-nwp.rev.ps.gz From jstrout@UCSD.EDU Thu Aug 24 17:06:17 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 24 Aug 95 17:06:15 -0500; AA07227 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 24 Aug 95 17:06:13 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa21971; 24 Aug 95 13:07:36 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa21969; 24 Aug 95 12:57:36 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07007; 24 Aug 95 12:57:30 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa18147; 24 Aug 95 11:21:58 EDT Received: from mailbox2.ucsd.edu by EDRC.CMU.EDU id aa06504; 24 Aug 95 11:21:03 EDT Received: from golgi (golgi.ucsd.edu [132.239.16.3]) by UCSD.EDU (8.6.12/8.6.9) with ESMTP id IAA14274; Thu, 24 Aug 1995 08:14:00 -0700 Received: by golgi (940816.SGI.8.6.9/940406.SGI) id IAA17254; Thu, 24 Aug 1995 08:13:07 -0700 Newsgroups: bionet.neuroscience,comp.ai,comp.ai.neural-nets,sci.cryonics Date: Thu, 24 Aug 1995 08:13:03 -0700 (PDT) From: Joseph Strout X-Sender: jstrout@golgi To: neuron@CATTELL20.psych.upenn.edu, Connectionists@cs.cmu.edu Subject: Neuron Emulation List (ANNOUNCEMENT) Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII ANNOUNCEMENT: Neuron Emulation List ----------------------------------- This message is to announce the opening of a new mailing list. Its purpose is the discussion of "neuron emulation" -- that is, the endeavor to recreate (in hardware or software) the functional properties of a particular neuron or small network of neurons. This is in contrast to the more common neural model, which recreates the general properties of an entire class of cells. Upon subscribing to the list, you will receive two copies of a document which is now out of date. The revised versions will probably not be posted until next week, due to personnel difficulties [read: postmaster on vacation]. However, the latest versions can be obtained from the Web site (below). Also at the web site is an archive of previous messages; the list has been open privately for a few weeks while we worked out bugs with the listserver. It seems to be running smoothly now, and as interest has been spreading, it seemed prudent to make the formal announcement. The NEL Web Site can be accessed through the following URL: http://sdcc3.ucsd.edu/~jstrout/nel/ Full instructions on using the list server, as well as etiquette guidelines and a preliminary FAQ, are available at that site. If you do not have access to the World Wide Web, contact me directly and I will forward you the relevant files. ,------------------------------------------------------------------. | Joseph J. Strout Department of Neuroscience, UCSD | | jstrout@ucsd.edu http://sdcc3.ucsd.edu/~jstrout/ | `------------------------------------------------------------------' From steven.young@psy.ox.ac.uk Fri Aug 25 13:53:50 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 25 Aug 95 13:53:45 -0500; AA18494 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 25 Aug 95 13:53:41 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa23063; 25 Aug 95 13:12:29 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa23061; 25 Aug 95 12:51:59 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa08311; 25 Aug 95 12:51:24 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa06850; 25 Aug 95 9:03:16 EDT Received: from oxmail2.ox.ac.uk by CS.CMU.EDU id aa12505; 25 Aug 95 9:02:08 EDT Received: from cogsci1.psych.ox.ac.uk by oxmail2.ox.ac.uk. with SMTP (PP) id <28683-0@oxmail2.ox.ac.uk.>; Fri, 25 Aug 1995 14:02:13 +0100 Received: (from young@localhost) by cogsci1.psych.ox.ac.uk (8.6.11/8.6.11) id OAA00656; Fri, 25 Aug 1995 14:01:25 +0100 From: Steven Young Message-Id: <199508251301.OAA00656@cogsci1.psych.ox.ac.uk> Subject: Oxford Summer School on Connectionist Modelling To: connectionists@cs.cmu.edu, dev-europe@durham.ac.uk, info-childs@poppy.psy.cmu.edu, psyling@psy.gla.ac.uk Date: Fri, 25 Aug 1995 14:01:24 +0100 (BST) Cc: Kim.Plunkett@psy.ox.ac.uk, Edmund.Rolls@psy.ox.ac.uk, susan.king@psy.ox.ac.uk, Steven.Young@psy.ox.ac.uk X-Mailer: ELM [version 2.4 PL24] Mime-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Length: 2941 This is a last minute call for participation for the Oxford Summer School on Connectionist Modelling. (This is extremely short notice. Apologies) Full details are given below. There are only 3 places left for this year's Summer School. The Summer School is a 2-week residential summer school which provides an introduction to connectionist modelling. Please pass on this information to people you may know who would be interested. The inital call for participation follows, and further information is available via the World Wide Web at the following URL: http://cogsci1.psych.ox.ac.uk/summer-school/ (Please ignore the closing date in the initial call for participation. If you know of people who might wish to attend then they could contact Sue King via email or phone -- details below). Here is the initial call for participation: Oxford Summer School on Connectionist Modelling Department of Experimental Psychology, University of Oxford 10-22 September 1995 Applications are invited for participation in a 2-week residential Summer School on techniques in connectionist modelling of cognitive and biological phenomena. The course is aimed primarily at researchers who wish to exploit neural network models in their teaching and/or research. It will provide a general introduction to connectionist modelling through lectures and exercises on Power PCs. The instructors with primary responsibility for teaching the course are Kim Plunkett and Edmund Rolls. No prior knowledge of computational modelling will be required though simple word processing skills will be assumed. Participants will be encouraged to start work on their own modelling projects during the Summer School. The cost of participation in the Summer School is 500 to include accommodation (bed and breakfast at St. John's College) and registration. Participants will be expected to cover their own travel and meal costs. A small number of graduate student scholarships may be available. Applicants should indicate whether they wish to be considered for a graduate student scholarship but are advised to seek their own funding as well, since in previous years the number of graduate student applications has far exceeded the number of scholarships available. If you are interested in participating in the Summer School, please contact: Mrs Sue King Department of Experimental Psychology South Parks Road University of Oxford Oxford OX1 3UD Tel: +44 (1865) 271 353 Email: sking@psy.ox.ac.uk Please send a brief description of your background with an explanation of why you would like to attend the Summer School (one page maximum) no later than 31st January 1995. Regards, Steven Young -- Facility for Computational Modelling in Cognitive Science McDonnell-Pew Centre for Cognitive Neuroscience, Oxford Department of Experimental Psychology, University of Oxford From stokely@atax.eng.uab.edu Fri Aug 25 20:51:51 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 25 Aug 95 20:51:48 -0500; AA21996 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 25 Aug 95 20:51:45 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa23381; 25 Aug 95 18:01:33 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa23379; 25 Aug 95 17:54:35 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa08487; 25 Aug 95 17:54:06 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa13948; 25 Aug 95 16:03:53 EDT Received: from Heath.DPO.UAB.EDU by CS.CMU.EDU id aa16982; 25 Aug 95 16:03:32 EDT Received: from engsun1.eng.uab.edu by Heath.DPO.UAB.EDU (4.0/SMI-4.0) id AA17829; Fri, 25 Aug 95 15:01:57 CDT Received: from atax.eng.uab.edu by engsun1.eng.uab.edu (4.0/SMI-4.0) id AA24997; Fri, 25 Aug 95 15:00:19 CDT Received: from [138.26.80.134] (shoat) by atax.eng.uab.edu (5.x/SMI-SVR4) id AA07943; Fri, 25 Aug 1995 15:02:52 -0500 Date: Fri, 25 Aug 1995 15:02:51 -0500 Message-Id: Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" To: Connectionists@cs.cmu.edu From: Ernest Stokely Subject: Faculty Position in Biomed. Engr. Tenure Track Faculty Position -------------------------- The Department of Biomedical Engineering at the University of Alabama at Birmingham has an opening for a tenure-track faculty member. The opening is being filled as part of a Whitaker Foundation Special Opportunities Award for a training and research program in functional and structural imaging of the brain. Candidates are particularly sought in neurosystems modelling, biological neural networks,computational neurobiology, or other multidisciplinary areas that combine neurobiology and imaging. More senior candidates and candidates with established research programs are of particular interest, in order that the UAB program can be quickly established. The person selected for this position will be expected to form active research collaborations with other units in the medical center part of the UAB campus. Candidates should have a Ph.D. degree in an appropriate field, and must be a U.S. citizen or have permanent residency in the U.S. The search will be continued until the position is filled. UAB is an autonomous campus within the University of Alabama system. UAB faculty currently are involved in over $180 million of externally funded grants and contracts. A 4.1 Tesla clinical NMR facility for cardiovascular and brain research, several other small-bore MR systems, a Philips Gyroscan system, and a team of research scientists and engineers working in various aspects of MR imaging and spectroscopy are housed only 200 meters from the School of Engineering. In addition, the brain imaging project will involve the opportunity for collaborations with members of the Neurobiology Research Center, a new Cognitive Science Graduate Program, as well as faculty members from the Departments of Neurology, Psychiatry, and Radiology. To apply send a letter of application, a current curriculum vitae, and three letters of reference to Dr. Ernest Stokely, Department of Biomedical Engineering, BEC 256, University of Alabama at Birmingham, Birmingham, Alabama 35294-4461. The University of Alabama at Birmingham is an equal opportunity, affirmative action employer, and encourages applications from women and minorities. Ernest Stokely Chair, Department of Biomedical Engineering BEC 256 University of Alabama at Birmingham Birmingham, Alabama 35294-4461 Internet: stokely@atax.eng.uab.edu FAX: (205) 975-4919 Phone: (205) 934-8420