From jordan@psyche.mit.edu Sun May 14 22:15:38 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Sun, 14 May 95 22:15:36 -0500; AA13105 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sun, 14 May 95 22:15:32 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa10431; 14 May 95 21:27:02 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10429; 14 May 95 21:07:07 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa06116; 14 May 95 21:06:18 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id ab16100; 14 May 95 18:58:03 EDT Received: from PSYCHE.MIT.EDU by CS.CMU.EDU id aa15951; 14 May 95 18:57:29 EDT Received: by psyche.mit.edu (4.1/DA1.0.4) id AA29442; Sun, 14 May 95 18:57:23 EDT Date: Sun, 14 May 95 18:57:22 EDT From: Michael Jordan To: connectionists@cs.cmu.edu Subject: Tech Report Available: EM algorithm Message-Id: FTP-host: psyche.mit.edu FTP-file: pub/jordan/AIM-1520.ps.Z The following paper is now available by anonymous ftp. ================================================================== On Convergence Properties of the EM Algorithm for Gaussian Mixtures (10 pages) Lei Xu and Michael I. Jordan CUHK and MIT Abstract: We build up the mathematical connection between the ``Expectation-Maximization'' (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix $P$, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of $P$ and provide new results analyzing the effect that $P$ has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models. ================================================================== From fwang@edzo.ucs.ualberta.ca Mon May 15 04:43:02 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 15 May 95 04:42:57 -0500; AA27377 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Mon, 15 May 95 04:42:50 -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 OAA22304; Mon, 15 May 1995 14:20:07 +0800 Message-Id: <199505150620.OAA22304@cs.uwa.oz.au> From: Feng Wang To: reinforce@cs.uwa.edu.au Subject: RL for time-delay system? Date: Sun, 14 May 1995 14:05:56 -0600 Hi, RL members, I have a question about using RL in time-delay system. Suppose a dynamical system has a time delay of 3 steps, i.e., when input is at time k, the system only receives this input at time k+3 and changes its dynamics. Then how can we use RL to adjust the parameter at time k? Say if we have a large positive action at time k, and receive a large positive reinforcement at time k+1, then using RL algorithm, delta_w=action(k)*reinforcement(k+1), so parameters should be changed to positive. But the problem is this positive reinforcement at time k+1 has nothing to do with the action at k, it's actually the result of action at time k-2. We could use TD(lemda) to HOLD the eligibility trace of action(k-2) at time k. But TD(lemda) gives more eligibility to action(k-1) and action(k) which have nothing to do with reinforcement(k+1). This is a problem. Any idea? or simply delay the reinforcement for 3 steps? Feng Wang From massone@mimosa.eecs.nwu.edu Mon May 15 21:40:20 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 15 May 95 21:40:18 -0500; AA09765 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Mon, 15 May 95 21:40:15 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa11505; 15 May 95 20:09:22 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa11503; 15 May 95 19:59:16 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa06970; 15 May 95 19:58:59 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa25440; 15 May 95 11:20:45 EDT Received: from mimosa.eecs.nwu.edu by EDRC.CMU.EDU id aa28820; 15 May 95 11:19:46 EDT Received: by mimosa.eecs.nwu.edu (8.6.12/8.6.12) id KAA03259 for connectionists@cs.cmu.edu; Mon, 15 May 1995 10:19:38 -0500 Date: Mon, 15 May 1995 10:19:38 -0500 From: Lina Massone Message-Id: <199505151519.KAA03259@mimosa.eecs.nwu.edu> To: connectionists@cs.cmu.edu Subject: post-doc opening (forwarded) I am posting this message for a friend. Please do not send inquiries to me! L. Massone **************************************************** Motor Control Postdoctoral Fellow Start Date: September 1, 1995 I seek a postdoctoral fellow for an NSF funded study on the learning of coordination during complex multijoint voluntary actions. I use computer simulation and empirical methods to address phenomena and mechanisms that underlie the learning of coordination between balance, posture and voluntary task goals during multijoint pulls made by freely-standing humans. Two new, large movement analysis laboratories are available with full computerized capabilities for collecting and analyzing biomechanical and EMG data (force plates, motion analysis, load cells). Applicants must have completed their Ph.D. in motor systems neuroscience, engineering, kinesiology or a related discipline. Knowledge of biomechanics (including modeling), systems analysis, nonlinear dynamics, motor psychology or statistics are highly desirable. Good communication skills are a strong plus. Opportunities exist for participating in seminars and courses offered through the Institute for Neuroscience, Programs in Medical Biomechanics, Physiology, Programs in Physical Therapy, and other departments. Please send a letter of application (including career goals), vita and the names, addresses and phone numbers of two references to Wynne A. Lee, Ph.D., Programs in Physical Therapy, Northwestern University Medical School, 645 N. Michigan Ave., Chicago IL 60611-2814. Email: wlee@casbah.acns.nwu.edu ----------------------------------------------- Wynne A. Lee, Ph.D. Programs in Physical Therapy, and The Institute for Neuroscience Northwestern University Medical School 645 N. Michigan Avenue (Suite 1100) Chicago IL 60611-2814 voice: 312-908-6795 fax: 312-908-0741 email: wlee@casbah.acns.nwu.edu ----------------------------------------------- From lbl@nagoya.riken.go.jp Tue May 16 05:22:27 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 16 May 95 05:22:23 -0500; AA24267 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 16 May 95 05:22:20 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa11634; 15 May 95 22:47:44 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa11632; 15 May 95 22:35:42 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07070; 15 May 95 22:35:09 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa04859; 15 May 95 22:15:50 EDT Received: from rknss1.riken.go.jp by CS.CMU.EDU id aa26574; 15 May 95 22:15:41 EDT Received: from nagoya.riken.go.jp (nagoya [134.160.96.1]) by rknss1.riken.go.jp (8.6.10+2.4W/3.3W-RIKEN) with SMTP id LAA15068 for ; Tue, 16 May 1995 11:15:35 +0900 Received: from xian.riken.go.jp by nagoya.riken.go.jp (5.0/6.4J.6-NAGOYA.RIKEN) id AA28998; Tue, 16 May 1995 11:13:02 --900 Received: by xian.riken.go.jp (5.x/SMI-SVR4) id AA20492; Tue, 16 May 1995 11:16:09 +0900 Date: Tue, 16 May 1995 11:16:09 +0900 From: Bao-Liang Lu Message-Id: <9505160216.AA20492@xian.riken.go.jp> To: Connectionists@cs.cmu.edu Subject: Paper available: parallel and modular multi-sieving net Cc: lbl@nagoya.riken.go.jp X-Sun-Charset: US-ASCII Content-Length: 1588 FTP-host:archive.cis.ohio-state.edu FTP-file:pub/neuroprose/lu.multisieve.ps.Z The following paper is now available by anonymous ftp. ========================================================================== A Parallel and Modular Multi-sieving Neural Network Architecture for Constructive Learning Bao-Liang Lu (RIKEN) Koji Ito (Toyohashi Univ. of Tech.; RIKEN) Hajime Kita (Kyoto Univ.) Yoshikazu Nishikawa (Kyoto Univ.) Abstract: In this paper we present a parallel and modular multi-sieving neural network (PMSN) architecture for constructive learning. This PMSN architecture is different from existing constructive learning networks such as the cascade correlation architecture. The constructing element of the PMSNs is a compound modular network rather than a hidden unit. This compound modular network is called a sieving module (SM). In the PMSN a complex learning task is docomposed into a set of relatively simple subtasks automatically. Each of these subtasks is solved by a corresponding individual SM and all of these SMs are processed in parallel. (It will appear in the Proc. of Fourth International Conference on Artificial Neural Networks (ANN'95), Cambridge, UK, 26-28 June 1995. 6 pages. No hard copies available.) ============================================================================ Bao-Liang Lu --------------------------------------------- Bio-Mimetic Control Research Center, RIKEN 3-8-31 Rokuban, Atsuta-ku, Nagoya 456, Japan Phone: +81-52-654-9137 Fax: +81-52-654-9138 Email: lbl@nagoya.riken.go.jp From gbugmann@school-of-computing.plymouth.ac.uk Tue May 16 14:16:33 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 16 May 95 14:16:31 -0500; AA00480 Message-Id: <9505161916.AA24260@lucy.cs.wisc.edu> Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 16 May 95 14:16:27 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa11833; 16 May 95 1:42:15 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa11831; 16 May 95 1:34:17 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07272; 16 May 95 1:33:46 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa06550; 16 May 95 1:09:26 EDT Received: from sun2.nsfnet-relay.ac.uk by CS.CMU.EDU id aa27492; 16 May 95 1:08:43 EDT Via: uk.ac.plymouth.school-of-computing; Tue, 16 May 1995 06:04:07 +0100 Date: Mon, 15 May 1995 11:00:26 +0100 (BST) From: "Guido.Bugmann xtn 2566" Subject: Position for Research Assistant To: connectionists@cs.cmu.edu, comp-ai.edu.utexas.cs@school-of-computing.plymouth.ac.uk, aisb@cogs.sussex.ac.uk University of Plymouth School of Computing Neurodynamics Research Group Postgraduate Research Assistant Applications are invited for a University-funded three-year postgraduate research assistant, to carry out an investigation within a broad range of topics related to the development of novel, biologically-inspired, neural network based, learning control systems, with particular application to the control of autonomous mobile robots. The range of work extends from theoretical studies of cognition and intelligent behaviour, through computational modelling of brain function, to the construction of neural network based controllers. The research will be carried out within the Neurodynamics Research Group of the School of Computing, further details of which are given below. The research assistant will be required to register for a PhD degree (fees are waived for University staff) and to carry out limited teaching/demonstrating duties. We are looking for high quality candidates who have, or are in the process of completing, a first degree or masters degree in a relevant discipline, eg electronic/mechanical engineering, mathematics, psychology, cognitive science, who are willing or able to use computational tools for either simulation or real-time control, and who have a strong interest in pursuing research in neural networks/systems, adaptive learning systems, and control systems. The salary for the post will be on the University Research Assistant scale, with a salary in the range A39231 to 12756 pa., dependent upon age, qualifications, experience, etc. Informal discussions about the post can be held with Dr Guido Bugmann (e-mail: gbugmann@soc.plym.ac.uk; tel: 01752 232566). Applications (by mail or email) should comprise a CV, a short description of interests and the names of 2 referees. Applications should be sent to Guido Bugmann at the address below, as soon as possible. The position will stay open until a suitable candidate is found. ----------------------------- Dr. Guido Bugmann Neurodynamics Research Group School of Computing University of Plymouth Plymouth PL4 8AA United Kingdom ----------------------------- Tel: (+44) 1752 23 25 66 / 41 Fax: (+44) 1752 23 25 40 Email: gbugmann@soc.plym.ac.uk ----------------------------- The Neurodynamics Research Group - Background Information The aim of this group is to investigate and develop computational neural models of brain behaviour in sensory perception, learning, memory and motor action planning and generation, and to use these models to develop novel artificial systems for intelligent sensory-motor control, eg of autonomous robots. The group was started in September 1991 and is led by Professor Mike Denham. Researchers in the group currently include a postdoctoral University Research Fellow, Dr Guido Bugmann, who has an international reputation in the field of neural dynamics, an EPSRC-funded postdoctoral Research Fellow, Dr Raju Bapi, who was a member of Prof Dan Levine's research group at the University of Texas, and who has expertise in the modelling of frontal lobe behaviour. The Group also has one University-funded Research Assistant and four research students The Group was also recently expanded by the appointment of a Senior Lecturer in Artificial Intelligence, Dr Sue McCabe, who was previously at the Royal Naval Engineering College and has expertise in AI, intelligent control and intelligent sensing, especially neural network models of auditory processing. The Group was awarded an EPSRC research grant, starting in August 1994, to investigate a novel biologically-inspired architecture for an intelligent control system, which is a collaborative project with Professor John Taylor and the Centre for Neural Networks at Kings College London. So far, the Group have been working on specific parts of the proposed integrated learning control system and have been able to contribute significantly to knowledge on visual information processing and on planning. As a result of our work over the last year, we have now begun to define the approach necessary for solving the deep theoretical and practical problems of integrating the various parts of the proposed system, based around an "sensory-action" approach to object perception and recognition and to the learning of spatial maps and adaptive behaviours for changing control objectives and environments. A novel neural network based system for control of an autonomous mobile robot has been developed and a simulation has been constructed using the Cortex-Pro system on a 486 PC. This simulated system controls currently a real robot with video camera and provides a practical working example of the basic architecture of the proposed learning control system. The intention is to build more advanced and detailed models of individual modules into the system as a result of parallel conceptual and theoretical research, eg into perception, learning and motor planning, in the Group. Recent publications: "A model for latencies in the visual system" Bugmann, G. and Taylor J.G. (1993) Proc. 3rd Conf. on Artificial Neural Networks (ICANN'93, Amsterdam), Gielen S. and Kappen B. (eds), p.165-168. "Modelling of the high firing variability of real cortical neurons with the temporal noisy-leaky integrator neuron model" Christodoulou C., Clarkson T., Bugmann G. and Taylor J.G. (1994) Proc. IEEE Int. Conf. on Neural Networks (ICNN'94) part of the World Congress on Computational Intelligence (WCCI'94), Orlando, Florida, USA, 2239-2244.. "An artificial neural network architecture for multiple temporal sequence processing" McCabe S L and Denham M J (1994) Proc World Congress on Neural Networks (WCNN'94), San Diego, California, USA, 738-743. "Role of short-term memory in visual information processing" Bugmann, G. and Taylor J.G. (1994) Proc. of Int. Symp. on Dynamics of Neural Processing, Washington, DC, USA, 132-136. "Learning to control intelligently" Denham, M J (1994) Proc. IEE Int Conf Control'94, Warwick, UK (plenary paper) "Route finding by neural net" Bugmann G, Taylor J G and Denham M J (1995) in Taylor JG (ed) Neural Networks, Alfred Waller L, Henley on Thames, pp217-230. "Robot control using temporal sequence learnin" Denham M J and McCabe S L (1995) Proc. World Congress on Neural Networks (WCNN'95), Washington D.C., USA (accepted for presentation) "Segmentation of the auditory scene" McCabe S L and Denham M J(1995) Proc. World Congress on Neural Networks (WCNN'95), Washington D.C., USA (accepted for presentation) ------------------------------------------------------------------- From p.j.b.hancock@psych.stir.ac.uk Tue May 16 14:16:40 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 16 May 95 14:16:37 -0500; AA00486 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 16 May 95 14:16:31 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa12591; 16 May 95 12:13:06 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa12585; 16 May 95 11:59:32 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07808; 16 May 95 11:59:11 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa13208; 16 May 95 11:25:47 EDT Received: from bannock.stir.ac.uk by CS.CMU.EDU id aa00946; 16 May 95 11:24:36 EDT Received: from nevis.stir.ac.uk by bannock with SMTP (PP) id <05739-0@bannock>; Tue, 16 May 1995 16:23:31 +0100 Received: by nevis.stir.ac.uk ($Revision: 1.37.109.9 $/16.2) id AA1552850034; Tue, 16 May 1995 16:23:30 +0100 From: Peter Hancock Message-Id: <9505161523.AA1552850034@nevis.stir.ac.uk> Subject: Info theory workshop To: connectionists@cs.cmu.edu Date: Tue, 16 May 95 16:23:29 BST Mailer: Elm [revision: 70.85] Call for contributions. Workshop on Information Theory and the Brain. 4-5th September 1995, University of Stirling, Scotland What is the goal of sensory coding? What algorithms help the brain to achieve that goal? What is the information content of spiking in neurons? Where is the trade-off between redundancy and decorrelation? How do internal representations reflect the statistics of sensory input? How is input from different modalities combined? These are the kind of issues to be discussed. The main thrust of the workshop is in furthering our understanding of what is happening in the brain, but with an eye also to possible applications of such algorithms. Numbers will be limited to around 30, for reasons of space and informality. Costs still to be determined, but minimal (less than 50 pounds, excluding accomodation). It is hoped that a proceedings will be published after the event. Postgraduates are particularly welcome. Stirling is situated in the centre of Scotland, with easy access by road, rail, and international airports at Edinburgh and Glasgow. The Edinburgh International Festival and Fringe will still be in progress and well worth a visit. Submissions: Please submit a one-page abstract, preferably by email, to Peter Hancock, pjh@psych.stir.ac.uk, by 30th June 1995. We expect that most people attending will contribute in some form. Organising committee: Roland Baddeley (Oxford) Peter Foldiak (St. Andrews) Colin Fyfe (Paisley) Peter Hancock (Stirling) Jim Kay (SASS, Aberdeen) Mark Plumbley (King's College London) Further information from Peter Hancock, Department of Psychology, University of Stirling, FK9 4LA, UK. Phone (+44) 1786 467675. Fax (+44) 1786 467641 Email pjh@psych.stir.ac.uk Note: the first question above is borrowed from David Field: What is the Goal of Sensory Coding?, Field, D., Neural Computation 6, 559-601, 1994. -- ------------------------------------------------------- Peter Hancock Department of Psychology 0 0 Face University of Stirling | Research FK9 4LA, UK \_/ Group Phone 01786 467675 Fax 01786 467641 pjh@psych.stir.ac.uk http://nevis.stir.ac.uk/~pjh ------------------------------------------------------- From sutton@gte.com Wed May 17 03:28:52 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 17 May 95 03:28:48 -0500; AA07013 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Wed, 17 May 95 03:28:42 -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 MAA03679; Wed, 17 May 1995 12:53:14 +0800 Message-Id: <199505170453.MAA03679@cs.uwa.oz.au> From: sutton@gte.com (Rich Sutton) To: Feng Wang Cc: reinforce@cs.uwa.edu.au Subject: Re: RL for time-delay system? Date: Mon, 15 May 1995 18:35:51 -0500 Dear Feng Wang: >Any idea? or simply delay the reinforcement for 3 steps? Or better, delay the eligibility trace for 3 steps. Conceptually, the right thing to do is to shape your eligibility trace to the true temporal structure of the credit assignment. The standard decaying trace reflects the crude idea that the more recently something has occurred the more credit it should be given. If one has better knowledge, then it is natural to build that into the shape of the trace. For example, Klopf's DR model used an inverted-U shaped eligibility kernel, roughly to match animal learning data on the effect of the inter-stimulus interval. You might want to do something like that. Rich Sutton From fukuda@mein.nagoya-u.ac.jp Wed May 17 05:46:50 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 17 May 95 05:46:48 -0500; AA22337 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Wed, 17 May 95 05:46:40 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id MAA03744; Wed, 17 May 1995 12:57:33 +0800 Message-Id: <199505170457.MAA03744@cs.uwa.oz.au> From: fukuda@mein.nagoya-u.ac.jp (Toshio Fukuda) To: Reinforce@cs.uwa.edu.au Subject: ICEC'96 call for papers [connectionists] Date: Tue, 16 May 1995 11:15:07 +0900 Dear Sirs, Attached below is the Call For Papers of 1996 IEEE International Conference on Evolutionary Computation (ICEC'96), which will be held in May 20-22, 1996, Nagoya, Japan. I would like you to encourage your colleagues and yourself to submit the papers to ICEC'96. Sincerely yours, Toshio Fukuda General Chair, ICEC'96 =================================================================== CALL FOR PAPERS 1996 IEEE International Conference on Evolutionary Computation (ICEC'96) May 20-22, 1996, Nagoya, Japan Co-sponsored by IEEE Neural Network Council (NNC) and Society of Instrument and Control Engineers (SICE) Topics: Theory of evolutionary computation, Applications of evolutionary computation, Efficiency / robustness comparisons with other direct search algorithms, Parallel computer implementations, Artificial life and biologically inspired evolutionary computation, Evolutionary algorithms for computational intelligence, Comparisons between difference variants of evolutionary algorithms, Machine learning applications, Genetic algorithm and selforganization, Evolutionary computation for neural networks, Fuzzy logic in evolutionary algorithms Submission Procedure: Prospective authors are invited to submit papers related to the listed topics for oral or poster presentation. Five (5) copies of the paper must be submitted for review. Papers should be printed on letter size white paper, written in English in two-column format in Times or similar font style, 10 points or larger with 2.5 cm margins on all four sides. A length of four pages is encouraged, and a limit of six pages, including figures, tables and references will be enforced. Centered at the top of the first page should be the complete title of the paper and the name(s), affiliation(s) and address(es) of the author(s). All papers (except those submitted for special sessions - which may have different deadlines - see information on special sessions below) should be sent to: Toshio Fukuda, General Chair Nagoya University Dept. of Micro System Engineering and Dept. of Mechano-Informatics and Systems Furo-cho, Chikusa-ku, Nagoya 464-01, JAPAN Phone: +81-52-789-4478, Fax: +81-52-789-3909, Email: fukuda@mein.nagoya-u.ac.jp ICEC'96 will be organized in conjunction with the conference of Artificial Life (May 16-18, 1996, Kyoto, JAPAN). General Chair: Toshio Fukuda Nagoya University fukuda@mein.nagoya-u.ac.jp Program Co-chairs: There are several special sessions organized for the 3rd IEEE ICEC '96; so far these include: *********************************************************** Constrained Optimization, Constraint Satisfaction and EC *********************************************************** Organized by Gusz Eiben, chair (Utrecht University, gusz@cs.ruu.nl) Dave Corne (University of Edinburgh,dave@aifh.ed.ac.uk) Jurgen Dorn (Technical University of Vienna, dorn@vexpert.dbai.tuwien.ac.at) Peter Ross (University of Edinburgh, peter@aisb.ed.ac.uk) Evolutionary Computation has proved its merit in treating difficult problems in, for example, numerical optimization and machine learning. Nevertheless, problems where constraints on the search space (i.e., on the candidate solutions) play an important role have received relatively little attention. In real-world problems, however, the presence of constraints seems to be rather the rule than the exception. The class of constrained problems can be divided into Constraint Satisfaction Problems (CSP) and Constrained Optimization Problems (COP). This special session addresses both subclasses, and aims to explore the extent to which EC can usefully tackle problems of these kinds. All correspondence regarding this special session should be addressed to: A.E. Eiben Department of Computer Science, Utrecht University P.O.Box 80089, 3508 TB Utrecht, The Netherlands Phone: +31-(0)30-533619, Fax: +31-(0)30-513791, Email: gusz@cs.ruu.nl ******************************************** Evolutionary Artificial Neural Networks ******************************************** Organized by X. Yao (The University of New South Wales, xin@cs.adfa.oz.au) Evolutionary Artificial Neural Networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search algorithms. Three levels of evolution in EANNs have been studied recently, i.e., the evolution of connection weights, architectures, and learning rules. Major issues in the research of EANNs include their scalability, generalization ability and interactions among different levels of evolution. This special session will serve as a forum for both researchers and practitioners to discuss these important issues and exchange their latest research results/ideas in the area. All correspondence regarding this special session should be addressed to: Xin Yao Department of Computer Science, University College, The University of New South Wales Australian Defence Force Academy Canberra, ACT 2600, Australia Phone: +61 6 268 8819, Fax: +61 6 268 8581, Email: xin@csadfa.cs.adfa.oz.au ****************************************** Evolutionary Robotics and Automation ****************************************** Organized by J. Xiao (University of North Carolina, xiao@uncc.edu) More and more researchers are applying evolutionary computation techniques to challenging problems in robotics and automation, where classical methods fail to be effective. In addition to being vastly applicable to many hard problems, evolutionary concepts inspire many researchers as well as users to be fully creative in inventing their own versions of evolutionary algorithms for the specific needs of different domains of problems. This special session serves as a forum for exchanging research results in this growing interdisciplinary area and for encouraging further exploration of the fusion between evolutionary computation and intelligent robotics and automation. All correspondence regarding this special session should be addressed to: Jing Xiao Department of Computer Science, University of North Carolina - Charlotte Charlotte, NC 28223 Phone: (704) 547-4883, Fax: (704) 547-3516, Email: xiao@uncc.edu ************************* Genetic Programming ************************* Organized by John R. Koza (Stanford University , Koza@Cs.Stanford.Edu) Lee Spector (Hampshire College, LSPECTOR@hampshire.edu) Yuji Sato (Hitachi Ltd. Central Research Lab., yuji@crl.hitachi.co.jp) The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Genetic programming extends the genetic algorithm to the domain of computer programs. In genetic programming, populations of program are genetically bred to solve problems. Genetic programming is a domain-independent method for evolving computer programs that solves, or approximately solves, a variety of problems from a variety of fields, including many benchmark problems from machine learning and artificial intelligence such as problems of control, robotics, optimization, game playing, and symbolic regression (i.e., system identification, concept learning). Early versions of genetic programming evolved programs consisting of only a single part (i.e., one main program). All correspondence regarding this special session should be addressed to: John R. Koza Computer Science Department, Margaret Jacks Hall, Stanford University Stanford, California 94305-2140 USA Phone: 415-723-1517, Fax: 415-941-9430, Email: Koza@Cs.Stanford.Edu ********************************************** Self-adaptation in Evolutionary Algorithms ********************************************** Organized by Guenter Rudolph (ICD Informatik Centrum Dortmund e.V., Rudolph@LS11.InformatikUni-Dortmund.de) Evolutionary algorithms (EAs) with the ability to adapt internal strategic parameters (like population size, mutation distribution, type of recombination operator, selective pressure etc.) during the search process usually find better solutions than variants with fixed strategic parameters. Self-adaptation is very useful if different (fixed) parameter settings produce large differences in the solution quality of the algorithm. Most experiences are available for (real-coded) EAs whose individuals adapt their mutation distributions (or step sizes). Here, the property to adjust the step size is induced by competitive pressure among individuals. Evidently, self-adapting mechanisms can be realized by competing subpopulations as well. The potential of those EAs is essentially unexplored. All correspondence regarding this special session should be addressed to: Guenter Rudolph ICD Informatik Centrum Dortmund e.V. Joseph-von-Fraunhofer-Str. 20, D-44227 Dortmund, Germany Phone: +49-(0)231-9700-365, Fax: +49-(0)231-9700-959, Email: Rudolph@LS11.Informatik.Uni-Dortmund.de ********************************************** Evolutionary Algorithms and Fuzzy Systems ********************************************** Organized by Witold Pedrycz (University of Manitoba, pedrycz@ee.umanitoba.ca) Fuzzy sets (FS) and evolutionary algorithms have been already successfully applied to many areas including fuzzy control and fuzzy clustering. There are a number of facets of symbiosis between the technologies of FS and GA. On one hand evolutionary computation enriches the optimization environment for fuzzy systems. On the other, fuzzy sets supply a new macroscopic and domain-specific insight into the fundamental mechanisms of evolutionary algorithms (including fuzzy crossover, fuzzy reproduction, fuzzy fitness function, etc.). The objective of this session is to foster further interaction between researchers actively engaged in FS and GAs. All correspondence regarding this special session should be addressed to: Witold Pedrycz Department of Electrical and Computer Engineering, University of Manitoba Winnipeg Canada RT 2N2 Phone: (204) 474-8380, Fax: (204) 261-4639, Email: pedrycz@ee.umanitoba.ca ********************************************************************* BE DARWINIAN: MAKE YOUR EVOLUTIONARY-BASED OPTIMIZATION ALGORITHM COMPETE WITH ALL OTHERS ********************************************************************* Organised by Hugues Bersini and Marco Dorigo from IRIDIA - ULB - Brussels. A special competition session will be organized during the 1996 IEEE International Conference on Evolutionary Computing in which all candidate evolutionary-based algorithms will compete on benchmark problems of real and combinatorial optimization. The rules of this competition will be announced in a near future but basically they will present the benchmark problems together with some standard results (best solution, computer time to reach it, etc...) to at least reproduce (but hopefully improve) for being accepted to participate in the session. This competition first aims at clarifying a situation which is every day more confused (just look at the email on the GA list) on the real potentialities of evolutionary-based or evolutionary-inspired algorithms as compared with classical optimization algorithms (Hill-Climbers, Simplex, ..) and less classical ones (Simulated annealing, Tabu search). Secondly it aims at establishing a firm standard and a natural selectionist test for each improvements on previous algorithms which recurrently appear in conferences like ICGA, PPSN and IEEE. The winner algorithms will be joined together and presented in a book to be released following the conference. All correspondence regarding this special session should be addressed to: Hugues Bersini IRIDIA , cp 194/6, Universite Libre de Bruxelles 50, av. Franklin Roosevelt, 1050 Bruxelles - Belgium Phone:+32.2650.27.33, Fax:+32.2650.27.15, Email:bersini@ulb.ac.be Submission to Special Sessions: Four (4) copies of complete papers (6 pages maximum) should be submitted to each session organizer. All papers will be reviewed. ******************************************************************************** The deadline for proposals for organizing other special sessions during the 3rd IEEE ICEC '96 is August 20, 1995; submit your proposal to any Program Co-Chairs. ******************************************************************************** Program Committee: Treasurer: Chisato Numaoka ( Sony Computer Science Lab.) Publication Chair: David Fogel ( Natural Selection Inc.) Publicity Co-Chairs: Pierre Borne(Ministere de l'Education Nationale), Takanori Shibata(MEL) Local Arrangement Chair: Takeshi Furuhashi ( Nagoya Univ. ) *************************************** Prof. Toshio Fukuda Nagoya University, Dept. of Micro System Engineering & Dept. of Mechano-Informatics and Systems Furo-cho, Chikusa-ku, Nagoya 464-01 JAPAN phone: +81-52-789-4478 fax: +81-52-789-3909 / 3115 E-mail: fukuda@mein.nagoya-u.ac.jp *************************************** From S.Khebbal@cs.ucl.ac.uk Wed May 17 08:46:34 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 17 May 95 08:46:32 -0500; AA24243 Message-Id: <9505171346.AA20293@lucy.cs.wisc.edu> Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 17 May 95 08:46:27 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa12907; 16 May 95 20:08:53 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa12905; 16 May 95 19:57:48 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07965; 16 May 95 19:57:12 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa15095; 16 May 95 13:40:05 EDT Received: from bells.cs.ucl.ac.uk by CS.CMU.EDU id aa02242; 16 May 95 13:36:19 EDT Received: from stanage.cs.ucl.ac.uk by bells.cs.ucl.ac.uk with local SMTP id ; Tue, 16 May 1995 18:35:45 +0100 To: Connectionists@cs.cmu.edu Cc: ga-list@aic.nrl.navy.mil Subject: New Intelligent Hybrid Systems Book Date: Tue, 16 May 95 18:35:33 +0100 From: S.Khebbal@cs.ucl.ac.uk NEW BOOK ANNOUCEMENT INTELLIGENT HYBRID SYSTEMS S. GOONATILAKE and S. KHEBBAL University College London There is now a growing realisation in the intelligent systems community that many complex problems require hybrid solutions. Increasingly hybrid systems combining genetic algorithms, fuzzy logic, neural networks, and expert systems are proving their effectiveness in a wide variety of real-world problems. This timely book brings together leading researchers from the United States, Europe and Asia who are pioneering the theory and application of Intelligent Hybrid Systems. The book provides a definition of hybrid systems, summarises the current state of the art, and details innovative methods for integrating different intelligent techniques. Application examples are drawn from domains including industrial control, financial and business modelling, and cognitive simulation. The book is also intended to equip researchers, application developers and managers with key reference and resource material for the successful development of hybrid systems. CONTENTS: ======== Chap 1: Intelligent Hybrid Systems : Issues, Classes and Future Trends - Suran Goonatilake & Sukhdev Khebbal, University College London, UK. PART ONE: FUNCTION-REPLACING HYBRIDS ==================================== Chap 2: Fuzzy Controller Synthesis with Neural Network Process Models - Wendy Foslien & Tariq Samad, Honeywell SSDC, California, USA. Chap 3: Replacing the Pattern Matcher of a Expert System with a Neural Network - Henry Tirri, Univ. of Helsinki, Finland. Chap 4: Genetic Algorithms and Fuzzy Logic for Adaptive Process Control - Charles Karr, U.S. Bureau of Mines, USA. Chap 5: Neural Network Weight Selection Using Genetic Algorithms - David Montana, BBN Systems & Technologies Inc, USA. PART TWO: INTERCOMMUNICATING HYBRIDS ==================================== Chap 6: A Unified Approach For Engineering Design - David Powell, Michael Skolnick & Shi Shing Tong, GEC, USA. Chap 7: A Hybrid System for Data Mining - Randy Kerber, Brian Livezey, & Evangelos Simoudis, Lockheed AI Center, Palo Alto, USA. Chap 8: Using Fuzzy Pre-processing with Neural Networks for Chemical Process Diagnostic Problems - Casimer Klimasaukas, NeuralWare, USA. Chap 9: A Multi Agent Approach for the Integration of Neural Networks and Expert Systems - Andreas Scherer & Gunter Schlageter, Praktische Informatik, FernUniversitaet, Hagen, Germany. PART THREE: POLYMORPHIC HYBRIDS =============================== Chap 10: Integrating Symbol Processing Systems and Connectionist Networks - Vasant Honavar & Leonard Uhr, Iowa State University & Dept of Computer Science, University of Wisconsin-Madison, USA. Chap 11: Reasoning with Rules and Variables in Neural Networks - Venkat Ajjanagadde & Lokendra Shastri, University of Pennsylvania, USA. Chap 12: The NeurOagent: A Neural Multi-agent Approach for Modelling, Distributed Processing and Learning - Khai Minh Pham, InferOne, France. Chap 13: Genetic Programming of Neural Networks: Theory and Practice - Frederic Gruau, Grenoble, France. PART FOUR: DEVELOPING HYBRID SYSTEMS ==================================== Chap 14: Tools and Environments for Hybrid Systems - Sukhdev Khebbal & Danny Shamhong, University College London, UK. ISBN 0471 94242 1 300pp January 1995 (Sterling) 29.95/$47.95 John Wiley & Sons Ltd, Baffins Lane, Chichester, West Sussex, PO19 1UD, UK. (also offices in New York, Brisbane, Toronto, and Singapore) There is also a World Wide Web page on Intelligent Hybrid Systems at : http://www.cs.ucl.ac.uk/staff/skhebbal/ihs and for the Intelligent Hybrid Systems Book at : http://www.cs.ucl.ac.uk/staff/skhebbal/ihs/ihsbook.html =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= # MAIL ADDRESS : | EMAIL ADDRESS : # # Sukhdev Khebbal, | S.Goonatilke@cs.ucl.ac.uk # # Suran Goonatilake, | S.Khebbal@cs.ucl.ac.uk # # Department of Computer Science,|------------------------------------# # University College London, | TELEPHONE NUMBERS : # # Gower Street, | Voice: +44 (0)171 391 1329 # # London WC1E 6BT. | Fax : +44 (0)171 387 1397 # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= From markey@dendrite.cs.colorado.edu Wed May 17 08:46:39 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 17 May 95 08:46:37 -0500; AA24249 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 17 May 95 08:46:32 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa12924; 16 May 95 20:19:59 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa12910; 16 May 95 19:59:27 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07971; 16 May 95 19:58:42 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa15577; 16 May 95 14:13:17 EDT Received: from [128.138.193.2] by EDRC.CMU.EDU id aa05088; 16 May 95 14:11:46 EDT Received: (from markey@localhost) by dendrite.cs.colorado.edu (8.6.12/8.6.12) id MAA22596; Tue, 16 May 1995 12:11:27 -0600 Date: Tue, 16 May 1995 12:11:27 -0600 From: Kevin Markey Message-Id: <199505161811.MAA22596@dendrite.cs.colorado.edu> To: connectionists@cs.cmu.edu Subject: NSF cognitive & behavioral budget cuts Cc: markey@dendrite.cs.colorado.edu The following alert was originally posted in Info-Childes. Congress will act on the budget resolution by the end of this week. ------------------------------------------------------------------------- EMERGENCY ACTION ALERT >From the Federation of Behavioral, Psychological and Cognitive Sciences The House Budget Committee has recommended the complete elimination of NSF research funding for Psychology, Anthropology, Sociology, Linguistics, Political Science, Economics, Geography, Cognitive Science, Decision, Risk and Management Sciences, History of Science, and Statistical Research for the Behavioral and Social Sciences-- as NSF's contribution to balancing the Federal budget. There is no doubt that NSF funding will be cut in the effort to balance the budget. But to selectively wipe out the behavioral and social sciences goes far beyond simply saving money. This is the most important crisis these sciences have faced since Ronald Reagan attempted to eliminate the same sciences in the early 1980s. Action on this will happen very quickly. The Budget Committee approved the budget package on May 11. The vote on the package by the full House will happen sometime between the 15th and 18th of May. In all likelihood, the budget resolution will pass the House unaltered. The Appropriations Committee will be bound by the spending limits imposed by the Budget Committee. But it need not be bound by the particular cuts recommended by the Budget Committee! Unfortunately, the House leadership has also made it known that no program that lacks a current authorization will be funded. The National Science Foundation is not currently authorized. Efforts to pass its authorization failed last year in the Senate. The House Science Committee Chair, Robert Walker (R-PA) has said that as soon as the budget is passed, the Science Committee will proceed to report its authorizations which include, among other things, NSF, NASA, and the research programs of the Department of Energy. Robert Walker is also the Vice-Chair of the Budget Committee, and he played a key role in determining the selective cuts at NSF. In a news conference on May 12, Walker said that the Directorate containing the research programs mentioned above was created simply because it was "politically correct" and that it is now time to make a correction. This means that there is little chance the NSF authorization from his Committee will contain an authorization for the Social, Behavioral, and Economic Sciences Directorate. If the Committee does not authorize the Directorate, the Appropriations Committee cannot fund the research programs it contains. So scientists must pay close attention to actions of the Budget, Appropriations, and the authorizing committee. The only way the course of events can be changed is for concerned citizens to let their elected representatives know that they as voters to not approve of these ideological cuts masquerading as budget balancing measures. You must take it on yourself immediately to 1) write or call your own representative and senator's office to express your disapproval 2) send a copy of your letter to: Robert Walker, George Brown (ranking minority member of the Science Committee and a likely ally of behavioral and social scientists), Jerry Lewis (Chairman of the House Appropriations Subcommittee that appropriates money for the National Science Foundation). And this next thing is equally important: SEND, FAX OR EMAIL A COPY OF YOUR CORRESPONDENCE TO THE FEDERATION OF BEHAVIORAL, PSYCHOLOGICAL, AND COGNITIVE SCIENCES. We have to be able to monitor how great an impact behavioral and social scientists are having, and the only way we can do that is by keeping track of how many contacts from scientists congressional offices have received. Any letter to Congress may be addressed as follows: Representative's name, U.S. House of Representatives (or U.S. Senate) Washington, D.C. 20515 (House) or 20510 (Senate). The Federation email is federation@apa.org. Federation fax is (202) 336-6158. If you need more information, our telephone number is (202) 336-5920. 3) Help us get the word out. Please see that the anthropology, sociology, linguistics, economics, political science, cognitive science, and geography departments on your campus receive this action alert as well. 4) It is very important that elected representatives do not hear only from the scientists affected. If you have acquaintances in the physical or biological sciences or the university administration who would write a letter or make a phone call to an elected representative, do everything you can to get such a communication sent. Margaret Jean Intons-Peterson Department of Psychology Indiana University Bloomington, Indiana 47405 INTONS@INDIANA.EDU Phone: 812-855-3991 Fax: 812-855-4691 From jaap.murre@mrc-apu.cam.ac.uk Wed May 17 08:47:09 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 17 May 95 08:46:44 -0500; AA24255 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Wed, 17 May 95 08:46:39 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab12924; 16 May 95 20:22:49 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa12916; 16 May 95 20:00:18 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07976; 16 May 95 19:59:23 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa09262; 12 May 95 12:45:08 EDT Received: from sirius.mrc-apu.cam.ac.uk by EDRC.CMU.EDU id aa18482; 12 May 95 12:42:53 EDT Received: from rigel.mrc-apu.cam.ac.uk by sirius.mrc-apu.cam.ac.uk with Sendmail (8.6.12/V050201); Fri, 12 May 1995 17:40:47 +0100 Message-Id: <199505121640.RAA24847@sirius.mrc-apu.cam.ac.uk> From: Jaap Murre Date: Fri, 12 May 1995 17:40:47 +0100 To: connectionists-request@cs.cmu.edu Subject: Paper on connectivity of the brain The following paper has been added to our ftp-site: J.M.J. Murre, & D.P.F. Sturdy (submitted). The connectivity of the brain: multi-level quantitative analysis. Revised version submitted to Biological Cybernetics. Abstract We develop a mathematical formalism for calculating connectivity volumes generated by specific topologies with various physical packing strategies. We consider four topologies (full, random, nearest neighbor, and modular connectivity) and three physical models: (i) interior packing, where neurons and connection fibers are intermixed, (ii) sheeted packing where neurons are located on a sheet with fibers running underneath, and (iii) exterior packing where the neurons are located at the surfaces of a cube or sphere with fibers taking up the internal volume. By extensive cross-referencing of available human neuroanatomical data we produce a consistent set of parameters for the whole brain, the cerebral cortex, and the cerebellar cortex. By comparing these inferred values with those predicted by the expressions, we draw the following general conclusions for the human brain, cortex, cerebellum: (i) Interior packing is less efficient than exterior packing (in a sphere). (ii) Fully and randomly connected topologies are extremely inefficient. More specifically we find evidence that different topologies and physical packing strategies might be used at different scales. (iii) For the human brain at a macrostructural level, modular topologies on an exterior sphere approach the data most closely. (iv) On a mesostructural level, laminarization and columnarization are evidence of the superior efficiency of organizing the wiring as sheets. (v) Within sheets, microstructures emerge in which interior models are shown to be the most efficient. With regard to interspecies similarities and differences we conjecture (vi) that the remarkable constancy of number of neurons per underlying mm2 of cortex may be the result of evolution minimizing interneuron distance in grey matter, and (vii) that the topologies that best fit the human brain data should not be assumed to apply to other mammals, such as the mouse for which we show that a random topology may be feasible for the cortex. The paper is 39 pages, single spaced. The postscript file and its compressed versions are called: ftp://ftp.mrc-apu.cam.ac.uk/pub/nn/murre/connect.ps (940 Kb) ftp://ftp.mrc-apu.cam.ac.uk/pub/nn/murre/connect.ps.Z (327 Kb) ftp://ftp.mrc-apu.cam.ac.uk/pub/nn/murre/connect.zip (223 Kb) (with PKZIP 2.04g) -- Jaap Murre jaap.murre@mrc-apu.cam.ac.uk After 1 June 1995: pn_murre@macmail.psy.uva.nl From pazzani@super-pan.ICS.UCI.EDU Wed May 17 22:31:33 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Wed, 17 May 95 22:31:29 -0500; AA04166 Received: from paris.ics.uci.edu by lucy.cs.wisc.edu; Wed, 17 May 95 22:31:15 -0500 Received: from super-pan.ics.uci.edu by paris.ics.uci.edu id aa22286; 17 May 95 15:57 PDT To: ML-LIST:; Subject: Machine Learning List: Vol. 7, No. 9 Reply-To: ml@ics.uci.edu Date: Wed, 17 May 1995 15:32:41 -0700 From: Michael Pazzani Message-Id: <9505171557.aa22286@paris.ics.uci.edu> Machine Learning List: Vol. 7, No. 9 Wednesday, May 17, 1995 Contents: HTML versions of ML-LIST Doctoral Research Scholarships recent JAIR ML-related publications New list INDUCTIVE Harlequin,US&UK:Sr. Level PHD CS Dissertation available for ftp REMINDER: NIPS Paper Deadline May 20th Papers to be presented at ICGA6 3rd Albany Conferenence on Molecular Biology CFP: Autumn School in Connectionism and Neural Networks, Muenster Conference Announcement: IPMU'96 WWW page - ML95 GP workshop Call For Participation: Evolutionary Robotics Session at ICGA95 ICEC'96 call for papers The Machine Learning List is moderated. Contributions should be relevant to the scientific study of machine learning. Mail contributions to ml@ics.uci.edu. Mail requests to be added or deleted to ml-request@ics.uci.edu. Back issues may be FTP'd from ics.uci.edu in pub/ml-list/V/ or N.Z where X and N are the volume and number of the issue; ID: anonymous PASSWORD: URL- http://www.ics.uci.edu/AI/ML/Machine-Learning.html ---------------------------------------------------------------------- From: Michael Pazzani Subject: HTML versions of ML-LIST Date: Wed, 17 May 1995 13:25:46 -0700 We are in the process of making all ML-LISTs available in HTML format. A sample digest in HTML format can be found at http://www.ics.uci.edu/~mlearn/sample2.html The HTML version is created automatically from the teXt version, by inserting various html commands. It appears "preformatted" because a significant amount of ML-LIST usually is preformatted. If you have comments or suggestions, you can send them to Jack Muramatsu (jmuramat@ics.uci.edu) ------------------------------ From: Paul Refenes Subject: Doctoral Research Scholarships Date: Tue, 2 May 1995 11:04:59 BST Collaborative PhD Research Scholarships Department of Decision Science London Business School University of London The Department of Decision Science at London Business School is offering three scholarships on its Doctoral programme. Commencing in October 1995 the research areas will include Neural Networks, Non-parametric statistics, Financial Engineering, Simulation, Optimisation and Decision Analysis. Principled Model Selection for Neural Network Applications in Nonlinear Time Series: to utilise developments from multinomial,times series theory and from the non-parametric statistics field for developing distribution theories, statistical diagnostics, and test procedures for recurrent neural network model identification. The methodology will be used to develop models of nonlinear cointegration in equity markets and in telecommunications data. Advanced Decision Technology in Financial Risk Management: The use of advanced decision technologies such as neural networks, non parametric statistics and genetic algorithms for the development of financial risk management models in the currency and soft commodity markets. Our industrial collaborator has special interest on robust neural network models for hedging and arbitrage strategies in the currency, soft commodity and equity markets. Intelligent systems in Industry Modelling and Simulation Environments: the use of simulation for the development of business strategy and the facilitation of executive debate is now well established and popular. Neural network technology will be used for the development of "intelligent simulation agents" that can process the vast amount of data generated by the simulations and adapt their behaviour by learning from the feed back patterns. London Business School offers students enrolled in the doctoral programme core courses on Research Methodology, Statistical Analysis, as well as a choice of advanced specialised subject area courses including Financial Economics, Equity Investment, Derivatives Research, etc. Candidates with a strong background in mathematics, oprerations research, computer science, nonparametric statistics, and/or econometrics who wish to apply are invited to write with a copy of their CV to: Professor D. Bunn or Dr A-P. N. Refenes London Business School Regents Park, London NW1 4SA tel: ++ 44 171 262 5050 fax: ++ 44 171 728 78 75 The Department =========== The Department of Decision Sciences of the London Business School is actively involved in innovative multi-disciplinary research on the application of new business modelling methodologies to individual and organisation decision- making. In seeking to extend the effectiveness of conventional methods of management science, statistical methods and decision support systems, with the latest generation of software platforms, artificial intelligence, neural networks, genetic algorithms and computationally intensive methods, the research themes of the department remain at the forefront of new practice. The NeuroForecasting Research Unit ================================== The NeuroForecasting Research Unit at London Business School is the major centre in Europe for research into neural networks, non-parametric statistics and financial engineering. With funding from the DTI, the European Commission and a consortium of leading financial institutions the research unit has attained a world-wide reputation for collaborative research. Doctoral students work in a team of highly motivated post-doctoral fellows, research fellows, doctoral students and faculty who are amongst Europe's leading authorities in the field. Advanced Decision Support Platforms =================================== The current trend in the design of decision support is towards a synthesis of multiple approaches and integration of business modelling techniques (optimisation with simulation, forecasting with decision analysis, etc. Using object-oriented software architectures, the group has developed innovative approaches for model structuring, strategic analysis, forecasting and decision- analytic procedures. Several companies and the ESRC are currently supporting this work. ------------------------------ From: Steve Minton Subject: recent JAIR ML-related publications Date: Wed, 3 May 95 14:31:53 PDT Readers of this group may be interested in the following ML-related papers recently published in JAIR. Instructions for obtaining JAIR articles are given below. Ortega, J. (1995) "On the Informativeness of the DNA Promoter Sequences Domain Theory" (Research Note), Volume 2, pages 361-367. PostScript: volume2/ortega95a.ps (134K) Abstract: The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory. Turney, P.D. (1995) "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm", Volume 2, pages 369-409. PostScript: volume2/turney95a.ps (474K) compressed, volume2/turney95a.ps.Z (183K) Abstract: This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search. Donoho, S.K. and Rendell, L.A. (1995) "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach", Volume 2, pages 411-446. PostScript: volume2/donoho95a.ps (501K) compressed, volume2/donoho95a.ps.Z (179K) Online Appendix: volume2/donoho94a-appendix.tar.Z (150K), source code & data Abstract: Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction. The PostScript files are available via: -- comp.ai.jair.papers -- World Wide Web: The URL for our World Wide Web server is http://www.cs.washington.edu/research/jair/home.html -- Anonymous FTP from either of the two sites below: CMU: p.gp.cs.cmu.edu directory: /usr/jair/pub/volume2 Genoa: ftp.mrg.dist.unige.it directory: pub/jair/pub/volume2 -- automated email. Send mail to jair@cs.cmu.edu or jair@ftp.mrg.dist.unige.it with the subject AUTORESPOND, and the body GET VOLUME2/ORTEGA95A.PS or GET VOLUME2/TURNEY95A.PS or GET VOLUME2/DONOHO95A.PS (either upper or lowercase is fine). Note: Your mailer might find these files too large to handle. -- JAIR Gopher server: At p.gp.cs.cmu.edu, port 70. For more information about JAIR, check out our WWW or FTP sites, or send electronic mail to jair@cs.cmu.edu with the subject AUTORESPOND and the message body HELP, or contact jair-ed@ptolemy.arc.nasa.gov. ------------------------------ From: Kamat Subject: New list INDUCTIVE Date: Fri, 5 May 1995 22:54:28 -0300 (ADT) ****************************ANNOUNCEMENT ******************************** Announcing a new electronic mailing list called INDUCTIVE (Inductive Learning Group) ****************************ANNOUNCEMENT ******************************** This mailing list is initiated to provide a separate forum for discussing various scientific issues related to INDUCTIVE (LEARNING) PROCESSES. We strongly feel that these processes are of central importance to cognitive science in general and artificial intelligence (AI) in particular, and that so far they have not been given the attention and effort they deserve. Moreover, we feel that the success of the entire enterprise (of cognitive science) depends on the success of the effort to model the inductive learning processes understood sufficiently broadly. We also believe that the current (and the previous) subdivisions of cognitive psychology and AI impedes (and has impeded) the progress of both enterprises, since there are serious reasons to believe that all cognitive processes are built on top of the inductive learning processes. We cordially invite various researchers from the above two disciplines (including those working in Pattern Recognition and Neural Networks) to join this supervised mailing list. As a first question we propose to discuss the very definition of the inductive learning process: Inductive learning is a process by means of which, given a finite positive training set C+ from a possibly infinite class (or category) C and a finite set C- from the complement of C, an agent is able to reach a state (of inductive generalization) which allows it to form an idea about, and REPRESENTATION of, the class C. This state, in turn, enables the agent to recognize a new object as belonging to class C or not. ****************************************************************************** The subscription to this list is free. This list will be moderated and we reserve the right to terminate the membership of those members who abuse the list. You may subscribe to the list by simply sending the following text to the address INDUCTIVE-SERVER@UNB.CA SUBSCRIBE INDUCTIVE FIRSTNAME LASTNAME ****************************************************************************** Lev Goldfarb Tel: 506-453-4566 Faculty of Computer Science Fax: 506-453-3566 University of New Brunswick E-mail: goldfarb@unb.ca Fredericton, N.B., E3B 5A3 Canada ------------------------------ From: Amy Ahearn Subject: Harlequin,US&UK:Sr. Level PHD CS Date: Mon, 8 May 1995 09:54:34 -0500 Wanted: We're looking for professionals who can apply techniques in data analysis, machine learning and decision-theoretic reasoning to real-world industrial applications. A Masters or PhD in computer science or equivalent experience is preferred. An ability to interact with clients to determine their needs would be very helpful. Top-notch programming ability is a definite plus. ============================================================================== "Some of the world's greatest software developers work at Harlequin." We are world leaders in symbolic processing, electronic publishing and related software applications. We write in C, C++, Dylan, Lisp, ML, Postscript, Prolog, and whatever else it takes. Including English. Harlequin's Electronic Publishing Division specialises in Postscript-compatible technology, producing versions of its own software for previewing images and text on screen or printing them either directly to hard copy or indirectly through a pre-press system. The Symbolic Processing Division develops software that helps to raise the level at which computers appear to operate by enabling computers to represent, change, manipulate, and communicate ideas, relationships, plans, and implications. From this environment come Harlequin's powerful software development tools, such as LispWorks, Knowledgeworks and CLIM, together with our unique application delivery tool, Transducer, as well as Harlequin's own vertical market applications. "We promise not to train you, but be ready to learn fast. And never be afraid to ask how, or why, or why not." What do you get if you take a bunch of compiler writers and application programmers, add some special hardware developers, and mix together with color imaging technologists, technical authors, customer support officers, product managers (and product marketing managers), QA administrators, sales and marketing, and a liberal smattering of other talented people? You get a global computing company that works together (in harmony mostly, but not always) to produce the world's finest software. Harlequin started life in Cambridge, UK in 1986. Today we have two offices in Cambridgeshire, one in Manchester, an office in Sydney, Australia, and three U.S. facilities in Cambridge, MA, Menlo Park, CA, and Seattle, WA. Further operations are planned as we grow to some 270 staff world wide. Please respond to: Ahearn@Harlequin.com, or fax (617) 252-6505 attn: Amy Ahearn, or snail to Harlequin, Inc., One Cambridge Center, Cambridge, MA 02142, Attn: Amy Ahearn ------------------------------ From: Terry Jones Subject: Dissertation available for ftp Date: Wed, 10 May 95 09:56:33 MDT Dissertation Available for ftp ============================== Evolutionary Algorithms, Fitness Landscapes and Search by Terry Jones Department of Computer Science University of New Mexico Albuquerque NM 87131 terry@santafe.edu A postscript version of my dissertation is now available for ftp from ftp.santafe.edu in the directory pub/terry/phd. The entire dissertation is in the file phd.ps.gz. When uncompressed (using gunzip), the postscript file is over 5MB. If you are on UNIX, you may want to print this using "lpr -s phd.ps" which will cause the printer software to make a symbolic link to the file rather than copying it (if you use -s you cannot remove phd.ps until the printing is done). If your printer cannot deal with a 5MB postscript file, you can ftp the dissertation in smaller pieces: part00.ps.gz (25 pages) Title, Abstract, Lists, Abbreviations etc. part01.ps.gz (12 pages) Introduction part02.ps.gz (34 pages) A Model of Landscapes part03.ps.gz (38 pages) Crossover, Macromutation & Population-based Search part04.ps.gz (36 pages) Reverse Hillclimbing part05.ps.gz (41 pages) Evolutionary Algorithms and Heuristic Search part06.ps.gz (19 pages) Related Work & Conclusions part07.ps.gz (44 pages) Appendices & Bibliography Mail me if you don't have gunzip, or don't know how to use anonymous ftp. If you do not have access to a postscript printer, you can get a hardcopy of the dissertation by requesting SFI TR 95-05-048 from mat@santafe.edu. These were just sent to be spiral bound, and will be available early next week. ABSTRACT -------- A new model of fitness landscapes suitable for the consideration of evolutionary and other search algorithms is developed and its consequences are investigated. Answers to the questions "What is a landscape?" "Are landscapes useful?" and "What makes a landscape difficult to search?" are provided. The model makes it possible to construct landscapes for algorithms that employ multiple operators, including operators that act on or produce multiple individuals. It also incorporates operator transition probabilities. The consequences of adopting the model include a "one operator, one landscape" view of algorithms that search with multiple operators. An investigation into crossover landscapes and hillclimbing algorithms on them illustrates the dual role played by crossover in genetic algorithms. This leads to the "headless chicken" test for the usefulness of crossover to a given genetic algorithm and to serious questions about the usefulness of maintaining a population. A "reverse hillclimbing" algorithm is presented that allows the determination of details of the basin of attraction of points on a landscape. These details can be used to directly compare members of a class of hillclimbing algorithms and to accurately predict how long a particular hillclimber will take to discover a given point. A connection between evolutionary algorithms and the heuristic search algorithms of Artificial Intelligence and Operations Research is established. One aspect of this correspondence is investigated in detail: the relationship between fitness functions and heuristic functions. By considering how closely fitness functions approximate the ideal for heuristic functions, a measure of search difficulty is obtained. This measure, fitness distance correlation, is a remarkably reliable indicator of problem difficulty for a genetic algorithm on many problems taken from the genetic algorithms literature, even though the measure incorporates no knowledge of the operation of a genetic algorithm. This leads to one answer to the question "What makes a problem hard (or easy) for a genetic algorithm?" The answer is perfectly in keeping with what has been well known in Artificial Intelligence for over thirty years. Terry Jones (terry@santafe.edu). ------------------------------ From: David Cohn Subject: REMINDER: NIPS Paper Deadline May 20th Date: Mon, 15 May 95 20:55:17 EDT Submissions mailed first-class from within the US or Canada, or sent from overseas via Federal Express/Airborne/DHL or similar carrier must be POSTMARKED by May 20, 1995. All other submissions must ARRIVE by this date. Mail submissions to: Michael Mozer NIPS*95 Program Chair Department of Computer Science University of Colorado Colorado Avenue and Regent Drive Boulder, CO 80309-0430 USA Mail general inquiries/requests for registration material to: NIPS*95 Registration Dept. of Mathematical and Computer Sciences Colorado School of Mines Golden, CO 80401 USA FAX: (303) 273-3875 e-mail: nips95@mines.colorado.edu Sites for LaTex style files: Copies of "nips.tex" and "nips.sty" are available via anonymous ftp at helper.systems.caltech.edu (131.215.68.12) in /pub/nips, b.gp.cs.cmu.edu (128.2.242.8) in /usr/dst/public/nips. The style files and other conference information may also be retrieved via World Wide Web at http://www.cs.cmu.edu:8001/Web/Groups/NIPS/NIPS.html ------------------------------ From: Robert Elliott Smith Subject: Papers to be presented at ICGA6 Date: Wed, 03 May 95 08:34:15 -0600 The organizers of the Sixth International Conference on Genetic Algorithms, to be held in Pittsburgh, PA, July 15-19, 1995, are please to present the following list of papers that will be presented at the conference. This list is followed by registration information for the conference. =================== ICGA-95: PAPERS ACCECPTED FOR PRESENTATION SELECTION Generalized Convergence Models for Tournament- and (mu,lambda)-Selection Thomas Baeck A Mathematical Analysis of Tournament Selection Tobias Blickle, Lothar Thiele On Decentralizing Selection Algorithms Kenneth De Jong, Jayshree Sarma Finding Multimodal Solutions Using Restricted Tournament Selection Georges Harik Analysis of Genetic Algorithms Evolution under Pure Selection Filippo Neri, Lorenza Saitta MUTATION AND RECOMBINATION A New Class of Crossover Operators for Numerical Optimization Jaroslaw Arabas, Jan J. Mulawka, Jacek Pokrasniewicz On Multi-Dimensional Encoding/Crossover Thang N. Bui, Byung-Ro Moon On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation Nikolaus Hansen, Andreas Ostermeier, Andreas Gawelczyk The Nature of Mutation in Genetic Algorithms Robert Hinterding, Harry Gielewski, T. C. Peachey Crossover, Macromutation, and Population-based Search Terry Jones What Have You Done for Me Lately? Adapting Operator Probabilities in a Steady-State Genetic Algorithm Bryant A. Julstrom Metabits: Generic Endogenous Crossover Control Jim Levenick Toward More Powerful Recombinations Byung Ro Moon, Andrew B. Kahng Fuzzy Recombination for the Continuous Breeder Genetic Algorithm H.-M. Voigt, H. Muhlenbein, D. Cvetkovic EVOLUTIONARY COMPUTATION TECHNIQUES The Distributed Genetic Algorithm Revisited Theodore C. Belding Solving Constraint Satisfaction Problems Using a Genetic/Systematic Search Hybrid That Realizes When to Quit James Bowen, Gerry Dozier Enhancing GA Performance Through Incest Prohibitions Based on Ancestry Robert Craighurst, Worthy Martin A Comparison of Parallel and Sequential Niching Methods Samir W. Mahfoud Selectively Destructive Re-start Jonathan Maresky, Yuval Davidor, Daniel Gitler, Gad Aharoni, Amnon Barak Genetic Algorithms, Numerical Optimization, and Constraints Zbigniew Michalewicz, Sita S. Raghavan A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization Khim Peow Ng, Kok Cheong Wong When Seduction Meets Selection Edmund Ronald Population-Oriented Simulated Annealing: A Genetic/Thermodynamic Hybrid Approach to Optimization James M. Varanelli, James P. Cohoon FORMAL ANALYSIS OF EVOLUTIONARY COMPUTATION AND PROBLEM DIFFICULTY Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms Terry Jones, Stephanie Forrest Signal-to-noise, Crosstalk and Long Range Problem Difficulty in Genetic Algorithms Hillol Kargupta Efficient Tracing of the Behaviour of Genetic Algorithms using Expected Values of Bit and Walsh Products J.N. Kok, P. Floreen Optimization Using Replicators Anil Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka Epistasis in Genetic Algorithms: An Experimental Design Perspective Colin Reeves, Christine Wright Epistasis in Periodic Programs Stefan Voget Hyperplane Ranking in Simple Genetic Algorithms Darrell Whitley, Keith Mathias, Larry Pyeatt Building Better Test Functions D. Whitley, K. Mathias, S. Rana, J Dzubera GENETIC PROGRAMMING The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming David Andre Causality in Genetic Programming Dana H. Ballard, Justinian Rosca Solving Complex Problems with Genetic Algorithms Bertrand Daniel Dunay, Frederic E. Petry Strongly Typed Genetic Programming in Evolving Cooperation Strategies Thoms Haynes, Roger L. Wainwright, Sandip Sen, Dale A. Schoenefeld Temporal Data Processing Using Genetic Programming Hitoshi Iba, Hugo de Garis, Taisuke Sato Two Ways of Discovering the Size and Shape of a Computer Program to Solve a Problem John R. Koza Evolving Data Structures Using Genetic Programming W.B. Langdon Accurate Replication in Genetic Programming Nicholas Freitag McPhee, Justin Darwin Miller Complexity Compression and Evolution Peter Nordin, Wolfgang Banzhaf Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code Peter Nordin, Wolfgang Banzhaf CO-EVOLUTION AND EMERGENT ORGANIZATION Biological Symbiosis as a Metaphor for Computational Hybridization Jason M. Daida, Steven J. Ross, Brian C. Hannan Evolving Globally Synchronized Cellular Automata Rajarshi Das, James P. Crutchfield, Melanie Mitchell, James E. Hanson The Evolution of Emergent Organization in Immune System Gene Libraries Ron Hightower, Stephanie Forrest, Alan S. Perelson Co-evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces Hugues Juille The Symbiotic Evolution of Solutions and their Representations Jan Paredis A Coevolutionary Approach to Learning Sequential Decision Rules Mitchell A. Potter, Kenneth A. De Jong, John J. Grefenstette Methods for Competitive Co-evolution: Finding Opponents Worth Beating Christopher D. Rosin, Richard K. Belew EVOLUTIONARY COMPUTATION IN COMBINATION WITH MACHINE LEARNING OR NEURAL NETS Evolution in Multi-agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrapedal Robot Lawrence Bull, Terrence C. Fogarty Adaptive Distributed Routing using Evolutionary Fuzzy Control Brian Carse, Terry Fogarty, Alistair Munro Relational Schemata: A Way to Improve the Expressiveness of Classifiers Philippe Collard, Cathy Escazut The Mating Pool: A Testbed for Experiments in the Evolution of Symbol Systems Lawrence Davis, David Orvosh Genetic Algorithm Enlarges the Capacity of Associative Memory Akira Imada, Keijiro Araki A Genetic Algorithm for Optimizing Fuzzy Decision Trees Cezary Z. Janikow PLEASE: A Prototype Learning System using Genetic Algorithms Leslie Knight, Sandip Sen A Parallel Genetic Algorithm for Concept Learning Filippo Neri, Attilio Giordana Evolutionary Grown Semi-Weighted Neural Networks Steve G. Romaniuk Combining Genetic Algorithms with Memory Based Reasoning John W. Sheppard, Steven L. Salzberg Cellular Encoding Applied to Neurocontrol Darrell Whitley, Frederic Gruau, Larry Pyeatt EVOLUTIONARY COMPUTATION APPLICATIONS I Determining Factorization: A New Encoding Scheme for Spanning Trees Applied to the Probabilistic Minimum Spanning Tree Problem Faris N. Abuali, Roger L. Wainwright, Dale A. Schoenefeld A Hybrid Genetic Algorithm for the Maximum Clique Problem Thang Nguyen Bui, Paul H. Eppley Finding (Near-)Optimal Steiner Trees in Large Graphs Henrik Esbensen Solving Equal Piles with the Grouping Genetic Algorithm Emanuel Falkenauer A Study of Genetic Algorithm Hybrids for Facility Layout Problems Kazuhiro Kado, Dave Corne, Peter Ross An Efficient Genetic Algorithm for Job Shop Scheduling Problems Shigenobu Kobayashi, Isao Ono, Masayuki Yamamura A Comparative Study of Genetic Search Kihong Park Inference of Stochastic Regular Grammars by Massively Parallel Genetic Algorithms Markus Schwehm, Alexander Ost Genetic Algorithm Approach to the Search for Golomb Rulers Stephen W. Soliday, Abdollah Homaifar, Gary L. Lebby An Adaptive Clustering Method using a Geometric Shape for Vehicle Routing Problems with Time Windows Sam R. Thangiah EVOLUTIONARY COMPUTATION APPLICATIONS II Applying Genetic Algorithms to Outlier Detection Kelly D. Crawford, Roger L. Wainwright Design of Statistical Quality Control Procedures Using Genetic Algorithms Aristides T. Hatjimihail, Theophanes T. Hatjimihail A Segregated Genetic Algorithm for Constrained Structural Optimization R. Le Riche, C. Knopf-Lenoir, R.T. Haftka A Preliminary Study of Genetic Data Compression Wee K. Ng A Standard GA Approach to Native Protein Conformation Prediction Arnold L. Patton, W. F. Punch, III, E. D. Goodman Using GAs to Characterize Workloads Chrisila C. Pettey, Thomas D. Wagner, Lawrence W. Dowdy Development of the Genetic Function Approximation Algorithm David Rogers A Parallel Genetic Algorithm for Multi-objective Microprocessor Design Timothy J. Stanley, Trevor Mudge A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems Rupert Weare, Edmund Burke, Dave Ellilman Evolutionary Computation in Air Traffic Control Planning C.H.M. van Kemenade, C.F.W. Hendriks, J.N. Kok Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses Frank Vavak, Terence C. Fogarty, Philip Cheng ========= Registration Information: 6TH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS July 15-19, 1995 University of Pittsburgh Pittsburgh, Pennsylvania, USA CONFERENCE COMMITTEE Stephen F. Smith, Chair Carnegie Mellon University Peter J. Angeline, Finance Loral Federal Systems Larry J. Eshelman, Program Philips Laboratories Terry Fogarty, Tutorials University of the West of England, Bristol Alan C. Schultz, Workshops Naval Research Laboratory Alice E. Smith, Local Arrangements University of Pittsburgh Robert E. Smith, Publicity University of Alabama The 6th International Conference on Genetic Algorithms (ICGA-95) brings together an international community from academia, government, and industry interested in algorithms suggested by the evolutionary process of natural selection, and will include pre-conference tutorials, invited speakers, and workshops. Topics will include: genetic algorithms and classifier systems, evolution strategies, and other forms of evolutionary computation; machine learning and optimization using these methods, their relations to other learning paradigms (e.g., neural networks and simulated annealing), and mathematical descriptions of their behavior. The conference host for 1995 will be the University of Pittsburgh located in Pittsburgh, Pennsylvania. The conference will begin Saturday afternoon, July 15, for those who plan on attending the tutorials. A reception is planned for Saturday evening. The conference meeting will begin Sunday morning July 16 and end Wednesday afternoon, July 19. The complete conference program and schedule will be sent later to those who register. TUTORIALS ICGA-95 will begin with three parallel sessions of tutorials on Saturday. Conference attendees may attend up to three tutorials (one from each session) for a supplementary fee (see registration form). Tutorial Session I 11:00 a.m.-12:30 p.m. I.A Introduction to Genetic Algorithms Melanie Mitchell - A brief history of Evolutionary Computation. The appeal of evolution. Search spaces and fitness landscapes. Elements of Genetic Algorithms. A Simple GA. GAs versus traditional search methods. Overview of GA applications. Brief case studies of GAs applied to: the Prisoner's Dilemma, Sorting Networks, Neural Networks, and Cellular Automata. How and why do GAs work? I.B Application of Genetic Algorithms Lawrence Davis - There are hundreds of real-world applications of genetic algorithms, and a considerable body of engineering expertise has grown up as a result. This tutorial will describe many of those principles, and present case studies demonstrating their use. I.C Genetics-Based Machine Learning Robert Smith - This tutorial discusses rule-based, neural, and fuzzy techniques that utilize GAs for exploration in the context reinforcement learning control. A rule-based technique, the learning classifier system (LCS), is shown to be analogous to a neural network. The integration of fuzzy logic into the LCS is also discussed. Research issues related to GA-based learning are overviewed. The application potential for genetics-based machine learning is discussed. Tutorial Session II 1:30-3:00 p.m. II.A Basic Genetic Algorithm Theory Darrell Whitley - Hyperplane Partitions and the Schema Theorem. Binary and Nonbinary Representations; Gray coding, Static hyperplane averages, Dynamic hyperplane averages and Deception, the K-armed bandit analogy and Hyperplane ranking. II.B Basic Genetic Programming John Koza - Genetic Programming is an extension of the genetic algorithm in which populations of computer programs are evolved to solve problems. The tutorial explains how crossover is done on program trees and illustrates how the user goes about applying genetic programming to various problems of different types from different fields. Multi-part programs and automatically defined functions are briefly introduced. II.C Evolutionary Programming David Fogel - Evolutionary programming, which originated in the early 1960s, has recently been successfully applied to difficult, diverse real-world problems. This tutorial will provide information on the history, theory, and practice of evolutionary programming. Case-studies and comparisons will be presented. Tutorial Session III 3:30-5:00 p.m. III.A Advanced Genetic Algorithm Theory Darrell Whitley - Exact Non-Markov models of simple genetic algorithms. Markov models of simple genetic algorithms. The Schema Theorem and Price's Theorem. Convergence Proofs, Exact Non-Markov models for permutation based representations. III.B Advanced Genetic Programming John Koza - The emphasis is on evolving multi-part programs containing reusable automatically defined functions in order to exploit the regularities of problem environments. ADFs may improve performance, improve parsimony, and provide scalability. Recursive ADFs, iteration-performing branches, various types of memories (including indexed memory and mental models), architecturally diverse populations, and point typing are explained. III.C Evolution Strategies Hans-Paul Schwefel and Thomas Baeck - Evolution Strategies in the context of their historical origin for optimization in Berlin in the 1960s. Comparison of the computer-versions (1+1) and (10,100) ES with classical optimum seeking methods for parameter optimization. Formal descriptions of ES. Global convergence conditions. Time efficiency in some simple situations. The role of recombination. Auto-adaptation of internal models of the environment. Multi-criteria optimization. Parallel versions. Short list of application examples. GETTING TO PITTSBURGH The Pittsburgh International Airport is served by most of the major airlines. Information on transportation from the airport and directions to the University of Pittsburgh campus, will be sent along with your conference registration confirmation letter. LODGING University Holiday Inn, 100 Lytton Avenue two blocks from convention site $92/day (single) $9 /day parking charge pool (indoor), exercise facilities Reserve by June 18. Call 412-682-6200. Hampton Inn, 3315 Hamlet Street 12 blocks from convention site $72/day (single) free parking, breakfast, and one-way airport transportation Reserve by July 1. Call 412-681-1000. Howard Johnson's, 3401 Boulevard of the Allies 12 blocks from convention site $56/day (single) free parking and Oakland transportation pool (outdoor) Reserve by June 13. Call 412-683-6100. Sutherland Hall (dorm), University Drive-Pitt campus 10 blocks from convention site (steep hill) $30/day, single no amenities (phone, TV, etc.) shared bathroom Reserve by July 1. Call 412-648-1100. CONFERENCE FEES REGISTRATION FEE Registrations received by June 11 are $250 for participants and $100 for students. Registrations received on or after June 12 and walk-in registrations at the conference will be $295 for participants and $125 for students. Included in the registration fee are entry to all technical sessions, several lunches, coffee breaks, reception Saturday evening, conference materials, and conference proceedings. TUTORIALS There is a separate fee for the Saturday tutorial sessions. Attendees may register for up to three tutorials (one from each tutorial session). The fee for one tutorial is $40 for participants and $15 for students; two tutorials, $75 for participants and $25 for students; three tutorials, $110 for participants and $35 for students. The deadline to register without a late fee is June 11. After this date, participants and students will be assessed a flat $20 late fee, whether they register for one, two, or all three tutorials. CONFERENCE BANQUET Not included in the registration fee is the ticket for the banquet. Participants may purchase banquet tickets for an additional $30. Note - Please purchase your banquet tickets nowQyou will be unable to buy them upon arrival. GUEST TICKETS Guest tickets for the Saturday evening reception are $10 each; guest tickets for the conference banquet are $30 each for adults and $10 each for children. Note - Please purchase additional tickets now - you will be unable to buy them upon arrival. CANCELLATION/REFUND POLICY For cancellations received up to and including June 1, a full refund will be given minus a $25 handling fee. FINANCIAL ASSISTANCE FOR STUDENTS With support from the Naval Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, a limited fund has been set aside to assist students with travel expenses. Students should have their advisor certify their student status and that sufficient funds are not available. Students interested in obtaining such assistance should send a letter before May 22 describing their situation and needs to: Peter J. Angeline, c/o Advanced Technologies Dept, Loral Federal Systems, State Route 17C, Mail Drop 0210, Owego, NY 13827-3994 USA. TO REGISTER Early registration is recommended. You may register by mail, fax, or email using a credit card (MasterCard or VISA). You may also pay by check if registering by mail. Note: Students must also send with their registration a photocopy of their valid university student ID or a letter from a professor. Complete the registration form and return with payment. If more than one registrant from the same institution will be attending, make additional copies of the registration form. Mail ICGA 95 Department of Industrial Engineering University of Pittsburgh 1048 Benedum Hall Pittsburgh, PA 15261 USA Fax Fax the registration form to 412-624-9831 Email Receive email form by contacting: icga@engrng.pitt.edu Up-to-date conference information is available on the World Wide Web (WWW) http://www.aic.nrl.navy.mil/galist/icga95/ CALL FOR ICGA '95 WORKSHOP PROPOSALS ICGA workshop proposals are now being solicited. Workshops tend to range from informal sessions to more formal sessions with presentations and working notes. Each accepted workshop will be supplied with space and an overhead projector. VCRs might be available. If you are interested in organizing a workshop, send a workshop title, short description, proposed format, and name of the organizers to the workshop coordinator by April 15, 1995. Alan C. Schultz - schultz@aic.nrl.navy.mil Code 5510, Navy Center for Artificial Intelligence Naval Research Laboratory Washington DC 30375-5337 USA REGISTRATION FORM Prof / Dr / Mr / Ms / Mrs Name ______________________________________________________ Last First MI I would like my name tag to read _____________________________________________ Affiliation/Business ______________________________________________________ Address ______________________________________________________ City ______________________________________________________ State ___________________ Zip ________________________ Country_____________________________________________ Telephone (include area code) Business _______________________________ Home______________________________ FEES (all figures in US dollars) Conference Registration Fee By June 11 ___ participant, $250 ___ student, $100 =$_________ On or after June 12 ___ participant, $295 ___ student, $125 =$_________ July 15 Tutorials Select up to three tutorials, but no more than one tutorial per tutorial session. Tutorial Session I: ___I.A Introduction to Genetic Algorithms ___I.B Application of Genetic Algorithms ___I.C Genetics-Based Machine Learning Tutorial Session II: ___II.A Basic Genetic Algorithm Theory ___II.B Basic Genetic Programming ___II.C Evolutionary Programming Tutorial Session III: ___III.A Advanced Genetic Algorithm Theory ___III.B Advanced Genetic Programming ___III.C Evolution Strategies Tutorial Registration Fee By June 11 ___one tutorial: participant, $40 student, $15 ___two tutorials: participant, $75 student, $25 = $_________ ___three tutorials: participant, $110 student, $35 On or after June 12, participants and students add a $20 late fee for tutorials = $_________ Banquet Ticket (not included in the Registration Fee; no tickets may be purchased upon arrival) participants/adult guest #______ ticket(s) @ $30 = $_________ child #______ ticket(s) @ $10 = $_________ Additional Saturday reception tickets (no tickets may be purchased upon arrival) guest #______ ticket(s) @ $10 = $_________ TOTAL (US dollars) $____________ METHOD OF PAYMENT ___ Check (payable to the University of Pittsburgh, US banks only) ___ MasterCard ___ VISA #__________________________________________ Expiration Date ____________________ Signature of card holder ______________________________________________ Note: Students must submit with their registration a photocopy of their valid student ID or a letter from a professor. Mail ICGA 95, Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall, Pittsburgh, PA 15261 USA Fax 412-624-9831 Email To receive email form: icga@engrng.pitt.edu World Wide Web (WWW) For up-to-date conference information: http://www.aic.nrl.navy.mil/galist/icga95/ Robert Elliott Smith Department of Engineering Science and Mechanics Room 210 Hardaway Hall The University of Alabama Box 870278 Tuscaloosa, Alabama 35487 <> rob@comec4.mh.ua.edu <> (205) 348-1618 <> (205) 348-7240 <> http://hamton.eng.ua.edu/college/home/mh/faculty/rsmith/Web/smith.html ------------------------------ From: George Berg Subject: 3rd Albany Conferenence on Molecular Biology Date: Sat, 6 May 1995 14:46:02 -0400 (EDT) 3rd Albany Conference on Computational Biology "Phylogenetic & Structural Relationships Among Proteins" September 28-October 1, 1995 Rensselaerville Conference Center, Rensselaerville, NY This interdisciplinary meeting will be the third in our series of Albany Conferences on Computational Biology, and will be held September 28 - October 1, 1995 in Rensselaerville near Albany, New York. Like the two predecessors ("Converging Approaches in Computational Biology" in 1990 and "Patterns of Biological Organization" in 1992), the aim of this conference is to explore the computational tools and approaches being developed in diverse fields within biology, with emphasis this year on phylogeny as apparent in relationships on the molecular level. The conference will be designed to provide an environment for a frank and informal exchange among scientists and mathematicians that is not normally possible within the constraints of topical, single-discipline meetings. The theme of the conference, "Phylogenetic and Structural Relationships Among Proteins", will be developed in five sessions: 3-D Structural Relationships Among Proteins, Sequence-Structure Interface, Data Bases, and Phylogenetic Methods and Protein Trees (I,II). Leading specialists in the various disciplines have been invited, with the degree of involvement in novel computational approaches as one of the most important criteria of selection. Speakers will include: Steven Benner: ETH, Zurich, Switzerland Stephen Bryant: NIH, Computational Biology Branch Cyrus Chothia: MRC, Cambridge, England G. Brian Golding: McMaster University, Hamilton, Ontario David Hillis: University of Texas, Austin, TX Charles Lawrence: Wadsworth Center, Albany, NY Peter Munson: NIH, Analytical Biostatistics Section Caro-Beth Stewart: University at Albany Arlin Stoltzfus: Dalhousie Univ, Halifax, Nova Scotia Joel Sussman: Brookhaven National Lab, Protein Data Bank Shoshana Wodak: Univ Libre de Bruxelles Brussels, Belgium For further details, see the WWW document http://www.cs.albany.edu/albany_conference95 Registration information also can be obtained by directly contacting the conference coordinator: Carole A. Keith Center for Molecular Genetics & 1995 Albany Conference BIO 126 University at Albany 1400 Washington Avenue Phone: 518/442-4327 Albany, NY 12222 USA FAX: 518/442-4354 Email: carole@cnsibm.albany.edu Information about the meeting sessions and format can be obtained from the following members of the organizing committee: Joachim Frank (co-chair), Wadsworth Center 518-474-7002 joachim@orkney.ph.albany.edu Carmen Mannella (co-chair), Wadsworth Center 518-474-2462 carmen@orkney.ph.albany.edu Jacquelyn Fetrow 518-442-4389 jacque@isadora.albany.edu Charles Lawrence, Wadsworth Center 518-473-3382 lawrence@wadsworth.ph.albany.edu Caro-Beth Stewart, University at Albany 518-442-4342 cs812@csc.albany.edu ------------------------------ From: Gerhard Paass Subject: CFP: Autumn School in Connectionism and Neural Networks, Muenster Date: Mon, 8 May 1995 17:29:02 +0200 = = = H e K o N N 9 5 = = = Autumn School in C o n n e c t i o n i s m and N e u r a l N e t w o r k s October 2-6, 1995 Muenster, Germany Conference Language: German A comprehensive description of the Autumn School together with abstracts of the courses can be found at the following addresses: WWW: http://borneo.gmd.de/~hekonn anonymous FTP: ftp.gmd.de directory: Learning/neural/hekonn95 = = = O V E R V I E W = = = Artificial neural networks (ANN's) have in recent years been discussed in many diverse areas, ranging from the modelling of learning in the cortex to the control of industrial processes. The goal of the Autumn School in Connectionism and Neural Networks is to give a comprehensive introduction to conectionism and artificial neural networks and to give an overview of the current state of the art. Courses will be offered in five thematic tracks. (The conference language is German.) The FOUNDATION track will introduce basic concepts (A. Zell, Univ. Stuttgart), as well as present lectures on information processing in biological neural systems (G. Palm, Univ. Ulm), on the relationship between ANN's and fuzzy logic (R. Kruse, Univ. Braunschweig), and on genetic algorithms (S. Vogel, Univ. Cologne). The THEORY track is devoted to the properties of ANN's as abstract learning algorithms. Courses are offered on approximation properties of ANN's (K. Hornik, Univ. Vienna), the algorithmic complexity of learning procedures (M. Schmitt, TU Graz), prediction uncertainty and model selection (G. Paass, GMD St. Augustin), and "neural" solutions of optimization problems (J. Buhmann, Univ. Bonn). This year, special emphasis will be put on APPLICATIONS of ANN's to real-world problems. This track covers courses on vision (H.Bischof, TU Vienna), character recognition (J. Schuermann, Daimler Benz Ulm), speech recognition (R. Rojas, FU Berlin), industrial applications (B. Schuermann, Siemens Munich), robotics (K.Moeller, Univ. Bonn), and hardware for ANN's (U. Rueckert, TU Hamburg-Harburg). In the track on SYMBOLIC CONNECTIONISM, there will be courses on: knowledge processing with ANN's (F. Kurfess, New Jersey IT), hybrid systems in natural language processing (S. Wermter, Univ. Hamburg), connectionist aspects of natural language processing (U. Schade, Univ. Bielefeld), and procedures for extracting rules from ANN's (J. Diederich, QUT Brisbane). In the section on COGNITIVE MODELLING, we have courses on representation and cognitive models (G. Dorffner, Univ. Vienna), aspects of cognitive psychology (R. Mangold-Allwinn, Univ. Saarbruecken), self-organizing ANN's in the visual system (C. v.d. Malsburg, Univ. Bochum), and information processing in the visual cortex (J.L. v. Hemmen, TU Munich). In addition, there will be courses on PROGRAMMING and SIMULATORS. Participants will have the opportunity to work with the SESAME system (J. Kindermann, GMD St.Augustin) and the SNNS simulator (A.Zell, Univ. Stuttgart). ------------------------------ From: ipmu96@robinson.ugr.es Subject: Conference Announcement: IPMU'96 Date: Tue, 16 May 1995 12:57:49 UTC+0200 ***************************************************** * Preliminary Call for Paper * * * * IPMU'96 * * * * Granada, Spain, July 1-5, 1996 * ***************************************************** ======================================================== Organized by Departamento de Ciencias de la Computacion e Inteligencia Artificial. Universidad de Granada. Sponsored by Junta de Andalucia. Universidad de Granada. Ayuntamiento de Granada. ======================================================== ======================================================== Aims and Scope ======================================================== Organized at a regular two-year interval, the IPMU International Conference deals with the difficulties existing in the acquisition, representation, management and transmission of data in knowledge-based and decision-making systems. It brings together researches working on various methodologies for the management of uncertain information and provides a useful exchange between theorists and practitioners using these different methodologies. ======================================================== Topics of particular interest ======================================================== * Uncertainty Methods: Measures of Information and Uncertainty, Bayesian and Probabilistic Methods, Fuzzy Methods, Mathematical Theory of Evidence, Belief Networks, Chaos Theory. * Non-standard Logics: Non-monotonic Logics, Approximate Reasoning, Multivalued Logics, Modal Logics, Temporal Reasoning, Case-based Reasoning. * Knowledge Acquisition and Representation: Machine Learning, Inductive Methods, Commonsense Knowledge, Intelligent Databases and Information Systems. * Intelligent Systems: Fuzzy Control, Neural Networks, Genetic Algorithms and Evolutionary Computation, Expert Systems under Uncertainty, Decision Support Systems, Multicriteria and Group Decision Making, Pattern Recognition, Image Processing, Classification, Belief Updating and Inconsistency Handling. ======================================================== Address and Location ======================================================== IPMU'96 Dpto. Ciencias de la Computacion e Inteligencia Artificial. E.T.S.I. Informatica. Avda. Andalucia, 38 Universidad de Granada. 18071 Granada. Spain. Phone: +34.58.244019 Fax: +34.58.243317 e-mail: ipmu96@robinson.ugr.es e-mail for submissions: ipmu96-submissions@robinson.ugr.es URL: http://pirata.ugr.es/ipmu96.html Granada, a world-famous city, whose history spans over thousand years, also has outstanding features as a modern conference town. The Alhambra, the city's monuments, cultural and University traditions, as well as excellent leisure facilities, good restaurants, lively night life, the Sierra Nevada mountains and the Coast, all attract thousand of visitors to Granada every year. ======================================================== Submission Information ======================================================== Authors should submit three copies of each full paper by November 1st. There will be a six page (two columns) limit on the final versions of accepted papers. Papers will be carefully reviewed and authors will be notified on the acceptance/rejection by February 1st, 1996. Final camera-ready copies for publication will required by April 1st, 1996. Submissions may be sent by mail to the address included in this call. Electronic submissions are encouraged. To submit a paper electronically, send an e-mail to ipmu96-submissions@robinson.ugr.es including the following information (in this order): a) Paper title (plain text) b) Author's names, including professional status. c) Surface mail and e-mail address for a contact author (plain text) d) A short abstract, including keywords or topic indicators (plain text) e) Paper body (Postscript format) Proceedings will be available at the opening of the Conference. Relevant papers may be selected for publication in special issues of leading international journals. ======================================================== Dates and Deadlines ======================================================== Nov. 1 1995: Deadline for submission of papers. Feb. 1 1996: Notification of acceptance/rejection. Apr. 1 1996: Reception of final camera-ready. May 15 1996: Deadline for early registration. July 1-5 1996: CONFERENCE. Early Registration Fee: 60.000 pesetas (ptas). Late Registration Fee: 70.000 pesetas (ptas). ======================================================== Invited sessions ======================================================== A number of invited sessions on special topics will be included in the program of IPMU'96. Authors will be invited to contribute to these sessions, which will be chaired by recognized experts in these topics. Proposals to organize invited sessions are welcome to be considered by the committee until September 15th. ======================================================== Honorary President ======================================================== Lotfi A. Zadeh(University of California, Berkeley, USA) ======================================================== General Chairpersons Committee ======================================================== Bernardette Bouchon-Meunier (CNRS, France) Miguel Delgado (University of Granada, Spain) Jose Luis Verdegay (University of Granada, Spain) Maria Amparo Vila (University of Granada, Spain) Ronald R. Yager (Iona College, NY, USA) ======================================================== International Program Committee ======================================================== J. Aczel (Canada) J. Aguilar-Martin (France) J. Baldwin (UK) S. Barro (Spain) A. Blanco (Spain) H. Berenji (USA) J. Bezdek(USA) P. Bonissone (USA) P. Bosc (France) J.L. Castro(Spain) D. Dubois (France) F. Esteva (Spain) M. Fedrizzi (Italy) M.A. Gil (Spain) A. Gonzalez (Spain) S. Guiasu (Canada) J. Gutierrez (Spain) F. Herrera (Spain) K. Hirota(Japan) J. Jacas (Spain) J.Y. Jaffray (France) A. Kandel (USA) E.P. Klement (Austria) G. Klir (USA) R. Kruse (Germany) M.T. Lamata (Spain) H.L. Larsen (Denmark) S.L. Lauritzen (Denmark) R. Lopez de Mantaras (Spain) R. Marin (Spain) J. Montero (Spain) S. Moral (Spain) H. Nguyen (USA) S. Ovchinnikov(USA) J. Pearl (USA) H. Prade(France) I. Requena (Spain) A. Rocha (Brazil) E. Ruspini (USA) A. Sage (USA) E. Sanchez (France) G. Shafer (USA) P. Shenoy (USA) P. Smets (Belgium) M. Sugeno (Japan) S. Termini (Italy) A. Titli (France) E. Trillas (Spain) I.B. Turksen (USA) R. Valle (France) L. Valverde (Spain) T. Yamakawa (Japan) H.J. Zimmermann (Germany) ======================================================== Organizing Committee: ======================================================== S. Moral (President) A. Blanco J.L. Castro J.C. Cubero A. Gonzalez F. Herrera M.T. Lamata J.M. Medina O. Pons I. Requena J.M. Zurita ======================================================== PRE-REGISTRATION FORM ======================================================== If you want to be kept informed, please fill in this form and return it to the address included in this call. ........................ cut here ....................... Pre-Registration Form Surname: First Name: Mailing Address: Phone: Fax: E-Mail: _ |_| I am interested in attending the conference. _ |_| I am interested in submitting a paper. ------------------------------ From: rosca@cs.rochester.edu Subject: WWW page - ML95 GP workshop Date: Tue, 16 May 1995 16:42:17 -0400 For the latest news regarding the ML95 GP workshop, consult the following web page: http://www.cs.rochester.edu/users/grads/rosca/ml95gpw.html Justinian ------------------------------ Date: Wed, 17 May 95 13:47:42 EDT From: schultz@aic.nrl.navy.mil Subject: Call For Participation: Evolutionary Robotics Session at ICGA95 ICGA-95 WORKSHOP ON EVOLUTIONARY ROBOTICS at the 1995 International Conference on Genetic Algorithms (ICGA-95) 15-20 July, 1995 University of Pittsburgh Pittsburgh, PA USA In conjunction with the 1995 International Conference on Genetic Algorithms (ICGA-95), a workshop/panel discussion will be held on Evolutionary Robotics. Overlapping and complementary approaches to using evolutionary algorithms in the area of robotics have been seen in recent years. At the workshop, we would like to explore some of these approaches, and hopefully better understand similarities in and differences between them. We are interested in hearing from researchers working in the area of robotics and evolutionary algorithms (GA, ES, GP, EP, etc) who would like to take part in this workshop. The format of the workshop will be an overview of the field, followed by short presentations of current research, and then an open discussion session. Topics of interest include (but are not limited to) the use of evolutionary algorithms in: evolutionary development of robots robotic learning, both on-line and off-line robotic control coordination of multiple robots perception and mult-sensor integration Researchers interested in participating should send a description of their research (please limit this to 300 to 500 words) to the workshop organizer by 10 June. Workshop organizer: Alan C. Schultz schultz@aic.nrl.navy.mil ------------------------------ From: Toshio Fukuda Subject: ICEC'96 call for papers Date: Tue, 16 May 1995 11:15:07 +0900 CALL FOR PAPERS 1996 IEEE International Conference on Evolutionary Computation (ICEC'96) May 20-22, 1996, Nagoya, Japan Co-sponsored by IEEE Neural Network Council (NNC) and Society of Instrument and Control Engineers (SICE) Topics: Theory of evolutionary computation, Applications of evolutionary computation, Efficiency / robustness comparisons with other direct search algorithms, Parallel computer implementations, Artificial life and biologically inspired evolutionary computation, Evolutionary algorithms for computational intelligence, Comparisons between difference variants of evolutionary algorithms, Machine learning applications, Genetic algorithm and selforganization, Evolutionary computation for neural networks, Fuzzy logic in evolutionary algorithms Submission Procedure: Prospective authors are invited to submit papers related to the listed topics for oral or poster presentation. Five (5) copies of the paper must be submitted for review. Papers should be printed on letter size white paper, written in English in two-column format in Times or similar font style, 10 points or larger with 2.5 cm margins on all four sides. A length of four pages is encouraged, and a limit of six pages, including figures, tables and references will be enforced. Centered at the top of the first page should be the complete title of the paper and the name(s), affiliation(s) and address(es) of the author(s). All papers (except those submitted for special sessions - which may have different deadlines - see information on special sessions below) should be sent to: Toshio Fukuda, General Chair Nagoya University Dept. of Micro System Engineering and Dept. of Mechano-Informatics and Systems Furo-cho, Chikusa-ku, Nagoya 464-01, JAPAN Phone: +81-52-789-4478, Fax: +81-52-789-3909, Email: fukuda@mein.nagoya-u.ac.jp ICEC'96 will be organized in conjunction with the conference of Artificial Life (May 16-18, 1996, Kyoto, JAPAN). General Chair: Toshio Fukuda Nagoya University fukuda@mein.nagoya-u.ac.jp Program Co-chairs: There are several special sessions organized for the 3rd IEEE ICEC '96; so far these include: *********************************************************** Constrained Optimization, Constraint Satisfaction and EC *********************************************************** Organized by Gusz Eiben, chair (Utrecht University, gusz@cs.ruu.nl) Dave Corne (University of Edinburgh,dave@aifh.ed.ac.uk) Jurgen Dorn (Technical University of Vienna, dorn@vexpert.dbai.tuwien.ac.at) Peter Ross (University of Edinburgh, peter@aisb.ed.ac.uk) Evolutionary Computation has proved its merit in treating difficult problems in, for example, numerical optimization and machine learning. Nevertheless, problems where constraints on the search space (i.e., on the candidate solutions) play an important role have received relatively little attention. In real-world problems, however, the presence of constraints seems to be rather the rule than the exception. The class of constrained problems can be divided into Constraint Satisfaction Problems (CSP) and Constrained Optimization Problems (COP). This special session addresses both subclasses, and aims to explore the extent to which EC can usefully tackle problems of these kinds. All correspondence regarding this special session should be addressed to: A.E. Eiben Department of Computer Science, Utrecht University P.O.Box 80089, 3508 TB Utrecht, The Netherlands Phone: +31-(0)30-533619, Fax: +31-(0)30-513791, Email: gusz@cs.ruu.nl ******************************************** Evolutionary Artificial Neural Networks ******************************************** Organized by X. Yao (The University of New South Wales, xin@cs.adfa.oz.au) Evolutionary Artificial Neural Networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search algorithms. Three levels of evolution in EANNs have been studied recently, i.e., the evolution of connection weights, architectures, and learning rules. Major issues in the research of EANNs include their scalability, generalization ability and interactions among different levels of evolution. This special session will serve as a forum for both researchers and practitioners to discuss these important issues and exchange their latest research results/ideas in the area. All correspondence regarding this special session should be addressed to: Xin Yao Department of Computer Science, University College, The University of New South Wales Australian Defence Force Academy Canberra, ACT 2600, Australia Phone: +61 6 268 8819, Fax: +61 6 268 8581, Email: xin@csadfa.cs.adfa.oz.au ****************************************** Evolutionary Robotics and Automation ****************************************** Organized by J. Xiao (University of North Carolina, xiao@uncc.edu) More and more researchers are applying evolutionary computation techniques to challenging problems in robotics and automation, where classical methods fail to be effective. In addition to being vastly applicable to many hard problems, evolutionary concepts inspire many researchers as well as users to be fully creative in inventing their own versions of evolutionary algorithms for the specific needs of different domains of problems. This special session serves as a forum for exchanging research results in this growing interdisciplinary area and for encouraging further exploration of the fusion between evolutionary computation and intelligent robotics and automation. All correspondence regarding this special session should be addressed to: Jing Xiao Department of Computer Science, University of North Carolina - Charlotte Charlotte, NC 28223 Phone: (704) 547-4883, Fax: (704) 547-3516, Email: xiao@uncc.edu ************************* Genetic Programming ************************* Organized by John R. Koza (Stanford University , Koza@Cs.Stanford.Edu) Lee Spector (Hampshire College, LSPECTOR@hampshire.edu) Yuji Sato (Hitachi Ltd. Central Research Lab., yuji@crl.hitachi.co.jp) The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Genetic programming extends the genetic algorithm to the domain of computer programs. In genetic programming, populations of program are genetically bred to solve problems. Genetic programming is a domain-independent method for evolving computer programs that solves, or approximately solves, a variety of problems from a variety of fields, including many benchmark problems from machine learning and artificial intelligence such as problems of control, robotics, optimization, game playing, and symbolic regression (i.e., system identification, concept learning). Early versions of genetic programming evolved programs consisting of only a single part (i.e., one main program). All correspondence regarding this special session should be addressed to: John R. Koza Computer Science Department, Margaret Jacks Hall, Stanford University Stanford, California 94305-2140 USA Phone: 415-723-1517, Fax: 415-941-9430, Email: Koza@Cs.Stanford.Edu ********************************************** Self-adaptation in Evolutionary Algorithms ********************************************** Organized by Guenter Rudolph (ICD Informatik Centrum Dortmund e.V., Rudolph@LS11.InformatikUni-Dortmund.de) Evolutionary algorithms (EAs) with the ability to adapt internal strategic parameters (like population size, mutation distribution, type of recombination operator, selective pressure etc.) during the search process usually find better solutions than variants with fixed strategic parameters. Self-adaptation is very useful if different (fixed) parameter settings produce large differences in the solution quality of the algorithm. Most experiences are available for (real-coded) EAs whose individuals adapt their mutation distributions (or step sizes). Here, the property to adjust the step size is induced by competitive pressure among individuals. Evidently, self-adapting mechanisms can be realized by competing subpopulations as well. The potential of those EAs is essentially unexplored. All correspondence regarding this special session should be addressed to: Guenter Rudolph ICD Informatik Centrum Dortmund e.V. Joseph-von-Fraunhofer-Str. 20, D-44227 Dortmund, Germany Phone: +49-(0)231-9700-365, Fax: +49-(0)231-9700-959, Email: Rudolph@LS11.Informatik.Uni-Dortmund.de ********************************************** Evolutionary Algorithms and Fuzzy Systems ********************************************** Organized by Witold Pedrycz (University of Manitoba, pedrycz@ee.umanitoba.ca) Fuzzy sets (FS) and evolutionary algorithms have been already successfully applied to many areas including fuzzy control and fuzzy clustering. There are a number of facets of symbiosis between the technologies of FS and GA. On one hand evolutionary computation enriches the optimization environment for fuzzy systems. On the other, fuzzy sets supply a new macroscopic and domain-specific insight into the fundamental mechanisms of evolutionary algorithms (including fuzzy crossover, fuzzy reproduction, fuzzy fitness function, etc.). The objective of this session is to foster further interaction between researchers actively engaged in FS and GAs. All correspondence regarding this special session should be addressed to: Witold Pedrycz Department of Electrical and Computer Engineering, University of Manitoba Winnipeg Canada RT 2N2 Phone: (204) 474-8380, Fax: (204) 261-4639, Email: pedrycz@ee.umanitoba.ca ********************************************************************* BE DARWINIAN: MAKE YOUR EVOLUTIONARY-BASED OPTIMIZATION ALGORITHM COMPETE WITH ALL OTHERS ********************************************************************* Organised by Hugues Bersini and Marco Dorigo from IRIDIA - ULB - Brussels. A special competition session will be organized during the 1996 IEEE International Conference on Evolutionary Computing in which all candidate evolutionary-based algorithms will compete on benchmark problems of real and combinatorial optimization. The rules of this competition will be announced in a near future but basically they will present the benchmark problems together with some standard results (best solution, computer time to reach it, etc...) to at least reproduce (but hopefully improve) for being accepted to participate in the session. This competition first aims at clarifying a situation which is every day more confused (just look at the email on the GA list) on the real potentialities of evolutionary-based or evolutionary-inspired algorithms as compared with classical optimization algorithms (Hill-Climbers, Simplex, ..) and less classical ones (Simulated annealing, Tabu search). Secondly it aims at establishing a firm standard and a natural selectionist test for each improvements on previous algorithms which recurrently appear in conferences like ICGA, PPSN and IEEE. The winner algorithms will be joined together and presented in a book to be released following the conference. All correspondence regarding this special session should be addressed to: Hugues Bersini IRIDIA , cp 194/6, Universite Libre de Bruxelles 50, av. Franklin Roosevelt, 1050 Bruxelles - Belgium Phone:+32.2650.27.33, Fax:+32.2650.27.15, Email:bersini@ulb.ac.be Submission to Special Sessions: Four (4) copies of complete papers (6 pages maximum) should be submitted to each session organizer. All papers will be reviewed. ******************************************************************************** The deadline for proposals for organizing other special sessions during the 3rd IEEE ICEC '96 is August 20, 1995; submit your proposal to any Program Co-Chairs. ******************************************************************************** Program Committee: Treasurer: Chisato Numaoka ( Sony Computer Science Lab.) Publication Chair: David Fogel ( Natural Selection Inc.) Publicity Co-Chairs: Pierre Borne(Ministere de l'Education Nationale), Takanori Shibata(MEL) Local Arrangement Chair: Takeshi Furuhashi ( Nagoya Univ. ) *************************************** Prof. Toshio Fukuda Nagoya University, Dept. of Micro System Engineering & Dept. of Mechano-Informatics and Systems Furo-cho, Chikusa-ku, Nagoya 464-01 JAPAN phone: +81-52-789-4478 fax: +81-52-789-3909 / 3115 E-mail: fukuda@mein.nagoya-u.ac.jp *************************************** ------------------------------ End of ML-LIST (Digest format) **************************************** From btelfer@relay.nswc.navy.mil Thu May 18 09:00:20 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 18 May 95 09:00:15 -0500; AA20761 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 18 May 95 09:00:12 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa13918; 17 May 95 12:58:32 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa13916; 17 May 95 12:39:12 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa08872; 17 May 95 12:38:34 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa27243; 17 May 95 9:42:08 EDT Received: from relay.nswc.navy.mil by CS.CMU.EDU id aa02222; 17 May 95 9:41:55 EDT Received: from ulysses.nswc.navy.mil by relay.nswc.navy.mil (4.1/SMI-4.1) id AA09230; Wed, 17 May 95 09:41:37 EDT Received: from elrond.nswc.navy.mil by ulysses.nswc.navy.mil (4.1/SMI-4.1) id AA01538; Wed, 17 May 95 09:39:44 EDT Date: Wed, 17 May 95 09:39:44 EDT From: Brian Telfer Message-Id: <9505171339.AA01538@ulysses.nswc.navy.mil> To: connectionists@cs.cmu.edu Subject: Final Call for WCNN-95 Special Session (Submitted by Harold Szu) Call for Papers for WCNN-95 special Novel Results Session by June 15 1995 World Congress on Neural Networks, Washington DC 7/17-21/95 Highlights & Attractions: @ Keynote speaker: Dr. M. Nelson, White House/Office Science Tech. Policy, will speak on Federal Programs on Information Superhighway. @ 7 plenary talks by Kohonen, Alkon, Carpenter, Szu, Freeman, Taylor, Amari @ 19 sessions, 7 special sessions, Special Interest Group meetings @ Neural Network Industrial Enterprise Day (Monday) & Federal Clean Car Initiative @ 2-day Fuzzy Neural Networks Symposium @ 24 INNS University Short Courses (P. Werbos's replaces H. Szu's) @ NIH/FDA Biomedical Symposium, highlights e.g., Telemedicine @ WCNN-95 Golf Range Sunday Afternoon Competition, @ Student Volunteers Application, please contact via e-mail: Charles@seas.gwu.edu Program details can be obtained by contacting Talley Management at address below or 74577.504@compuserve.com. Background: The World Congress on Neural Networks is the only International Neural Network Conference on the North American Continent in 1995. Don't miss it. To encourage your active participation, here is a unique offer: The date is 1995 July 17-21 Washington DC: Renaissance Hotel($99/day; 800-228-9898, (202)962-4445 (fax)). WCNN is sponsored by the International Neural Network Society as an annual mechanism for Interdisciplinary Information Dissemination, in collaboration with IEEE, SPIE, NIH, FDA, ONR, APS, AAAI, AICE, APNNA, JNNS, ENNS, SME. IEEE members will enjoy the same low registration fee as INNS members ($75 Student members, $255 regular INNS or IEEE Members, registration must be sent prior to June 16 to TALLEY, 875 KINGS HIGHWAY, SUITE 200 WOODBURY, NJ 08096-3172, or 609-853-0411 FAX). Note that to enjoy the discount, your registration must be sent to Talley by June 16, but your technical paper must be sent directly to the Local Organization Committee address shown as follows. Accepted papers for WCNN-95 will be presented in "Novel Results" Session and published as a supplement to the paper proceedings available on-site. However, the final papers are not due until June 15 1995, a month before the DC Conference. It will give you plenty of time to report your most recent and exciting developments. (i) Submission Procedure: Paper format is: camera ready, 8.5x11 paper, 1" margins, single column, single spaced, minimum 10-pt font, 4 page limit ($20 per extra page). Cover letter should contain: full title of paper, corresponding and presenting authors, address, telephone and fax numbers, email address, preference of oral or poster presentation, audio-visual requirements. All poster presenters will give a 3 minute oral introduction (2 viewgraph maximum, including 1 quadchart containing authors/title, background, approach, results) to their poster during the oral session. (ii) Review Procedure: If a member of INNS Governing Board or SIGINNS Chair has already reviewed with an endorsement for acceptance for presentation in the submission letter, the paper will be accepted as it is. Send the original and one copy. The Local Organization Committee (LOC) will review it for the type of presentation and immediately inform the authors by e-mail or Fax ASAP. If not endorsed, the paper will be reviewed by the LOC. Send the original paper and five copies. (iii) All papers accepted for oral or poster presentation will be included in the session called Novel Results and will appear in a supplement to the WCNN-95 Proceedings and be distributed on-site. (iv) Best Poster Award will be chosen from all contributors who wish to be so considered. Best Poster Awards are rated according to: (a) Quality of Technical Content, (b) Quality of Oral Presentation, (c) Effectiveness of Poster Design, and the best three will be kept in a central area with all other winners throughout the conference. (v) Oral vs Poster: Oral presentation is prefered when a brand new result requires simultaneous peer review, when the main result can be presented in the limited time slot, and when a known speaker is capable to give a stimulating talk. Poster presentation is prefered if the author wishes to interact with the original inventors, if the result requires more than 15 minutes to do it justice, and if the paper requires nontraditional demo and tailoring to individual expertise. (vi) Submission address: Harold Szu WCNN-95 LOC Chair 9402 Wildoak Dr. Bethesda MD 20814. (301) 394-3097 (Office); (301) 394-1929 (Brian) (301) 394-3923 (Fax) e-mail: HSzu@Ulysses.NSWC.Navy.Mil In Summmary: Please encourage your colleagues that this is the only Conference in neural nets to attend to keep up with the interdisciplinary development related to the brain-style computing, Biomedical & Engineering Applications, Natural Intelligence, Mind & Body, Learning and Artificial Neural Network Models. (i) Governors & SIGINNS Chairs Guaranteed Acceptance,if reviewed by them. (ii) INNS & IEEE Identical Membership Discount Rate. (iii) Special Session: Novel Results DL: June 15 1995 (iv) Usual single column, single space, 12-pt font, 4 page limit. (vi) Papers accepted will be included in the WCNN Proceeding package. From maja@cs.brandeis.edu Thu May 18 09:00:27 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 18 May 95 09:00:24 -0500; AA20767 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 18 May 95 09:00:17 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa14220; 17 May 95 18:52:27 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa14199; 17 May 95 18:39:53 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa09122; 17 May 95 18:39:03 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa04654; 17 May 95 18:19:02 EDT Received: from garnet.cs.brandeis.edu by CS.CMU.EDU id aa07779; 17 May 95 18:18:22 EDT Received: (from maja@localhost) by garnet.cs.brandeis.edu (8.6.11/8.6.9) id SAA06588; Wed, 17 May 1995 18:18:38 -0400 Date: Wed, 17 May 1995 18:18:38 -0400 From: Maja Mataric Message-Id: <199505172218.SAA06588@garnet.cs.brandeis.edu> To: connectionists@cs.cmu.edu Subject: Conference Announcement and Call For Papers ============================================================================== Conference Announcement and Call For Papers FROM ANIMALS TO ANIMATS Fourth International Conference on Simulation of Adaptive Behavior (SAB96) Cape Cod, Massachusetts, USA, September 9-13, 1996 The objective of the conference is to bring together researchers in ethology, psychology, ecology, artificial intelligence, artificial life, robotics, and related fields so as to further our understanding of the behaviors and underlying mechanisms that allow natural and artificial animals to adapt and survive in uncertain environments. The conference will focus particularly on well-defined models, computer simulations, and robotics demonstrations, in order to help characterize and compare various organizational principles or architectures capable of inducing adaptive behavior in real animals or synthetic agents. Contributions treating any of the following topics from the perspective of adaptive behavior will receive special emphasis: Action selection Learning and development Perception and motor control Evolutionary computation Neural correlates of behavior Coevolutionary models Emergent structures and behaviors Parallel and distributed models Motivation and emotion Collective and social behavior Internal world models Autonomous robots Characterization of environments Applied adaptive behavior Authors should make every effort to suggest implications of their work for both natural and artificial animals. Papers which do not deal explicitly with adaptive behavior will be rejected. Submission Instructions Authors are requested to send five copies (hard copy only) of a full paper to the Program Chair (Pattie Maes) arriving no later than Feb 9th, 1996. Late submissions will not be considered. Papers should not exceed 10 pages (excluding the title page), with 1 inch margins all around, and no smaller than 10 pt (12 pitch) type (Times Roman preferred). The Web site listed below contains the latex .sty file producing the preferred format for submissions. Each paper must include a title page containing the following: (1) Full names, postal addresses, phone numbers, email addresses (if available), and fax numbers for each author, (2) A 100-200 word abstract, (3) The topic area(s) in which the paper could be reviewed (see list above). Camera ready versions of the papers, in two-column format, will be required by May 10th. Computer, video, and robotic demonstrations are also invited for submission. Submit a 2-page proposal plus a title page as above to the program chair. Indicate equipment requirements and relevance to the themes of the conference. Conference Chairs Pattie Maes, Program MIT Media Lab 20 Ames Street Rm 305 Cambridge, MA 02139 USA email: pattie@media.mit.edu Maja Mataric, Local Arrangements Volen Center for Complex Systems Computer Science Department Brandeis University Waltham, MA 02254 USA email: maja@cs.brandeis.edu Jean-Arcady Meyer, Publicity Groupe de Bioinformatique URA686.Ecole Normale Superieure 46 rue d'Ulm 75230 Paris Cedex 05 France email: meyer@wotan.ens.fr Jordan Pollack, Local Arrangements/Financial Volen Center for Complex Systems Computer Science Department Brandeis University Waltham, MA 02254 USA email: pollack@cs.brandeis.edu Herbert Roitblat, Financial Department of Psychology University of Hawaii 2430 Campus Road Honolulu, HI 96822 USA email: roitblat@uhunix.uhcc.hawaii.edu Stewart Wilson, Proceedings The Rowland Institute for Science 100 Edwin H. Land Blvd. Cambridge, MA 02142 USA email: wilson@smith.rowland.org (Tentative) Program Committee: M. Arbib, USA; R. Arkin, USA; R. Beer, USA; A. Berthoz, France; B. Blumberg, USA; L. Booker, USA; R. Brooks, USA; D. Cliff, UK; P. Colgan, Canada; T. Collett, UK; H. Cruse, Germany; J. Delius, Germany; A. Dickinson, UK; J. Ferber, France; D. Floreano, UK; N. Franceschini, France; S. Giszter, USA; S. Goss, Belgium; J. Hallam, UK; I. Harvey, UK; I. Horswill, USA; P. Husbands, UK; L. Kaelbling, USA; H. Klopf, USA; L-J. Lin, USA; M. Littman, USA; D. McFarland, UK; J. Millan, Spain; G. Miller, UK; R. Pfeifer, Switzerland; J. Slotine, USA; T. Smithers, Spain; O. Sporns, USA; J. Staddon, USA; L. Steels, Belgium; L. Stein, USA; F. Toates, UK; P. Todd, USA; S. Tsuji, Japan; W. Uttal, USA; D. Waltz, USA. Official Language: English Publisher: MIT Press/Bradford Books Important Dates =============== FEB 9, 1996: Submissions must be received APR 12: Notification of acceptance or rejection (via email) MAY 10: Camera ready revised versions due JUN 10: Early registration deadline AUG 8: Hotel reservations and regular registration deadline SEP 9-13: Conference dates General queries to: sab96@cs.brandeis.edu WWW Page: http://www.cs.brandeis.edu/conferences/sab96 ============================================================================== From tibs@pc-tibs.Stanford.EDU Thu May 18 09:00:32 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 18 May 95 09:00:30 -0500; AA20773 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 18 May 95 09:00:24 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa14407; 17 May 95 21:31:13 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa14405; 17 May 95 21:19:25 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa09201; 17 May 95 21:19:01 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa05912; 17 May 95 20:30:14 EDT Received: from Argus.Stanford.EDU by EDRC.CMU.EDU id aa11658; 17 May 95 20:29:16 EDT Received: from pc-tibs.Stanford.EDU (pc-tibs.Stanford.EDU [36.63.0.52]) by Argus.Stanford.EDU (8.6.11/8.6.6) with ESMTP id RAA17915 for <@Stanford.EDU:Connectionists@cs.cmu.edu>; Wed, 17 May 1995 17:29:12 -0700 Received: by pc-tibs.Stanford.EDU (8.6.8/930416.SGI) for Connectionists@cs.cmu.edu id RAA14900; Wed, 17 May 1995 17:28:08 -0700 Date: Wed, 17 May 1995 17:28:08 -0700 From: Rob Tibshirani Message-Id: <199505180028.RAA14900@pc-tibs.Stanford.EDU> To: Connectionists@cs.cmu.edu Subject: new paper The following paper (without figures) is now available at the ftp site utstat.toronto.edu in pub/bootpred.shar (shar postscript files). A paper copy with figures is available upon request from karola@playfair.stanford.edu Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule Bradley Efron Robert Tibshirani Stanford Univ Univ of Toronto A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? The traditional answer to this question is given by cross-validation. The cross-validation estimate of prediction error is nearly unbiased, but can be highly variable. This article discusses bootstrap estimates of prediction error, which can be thought of as smoothed versions of cross-validation. A particular bootstrap method, the $632+$ rule, is shown to substantially outperform cross-validation in a catalog of 24 simulation experiments. Besides providing point estimates, we also consider estimating the variability of an error rate estimate. All of the results here are nonparametric, and apply to any possible prediction rule. The simulations include ``smooth'' prediction rules like Fisher's Linear Discriminant Function, and unsmooth ones like Nearest Neighbors. ============================================================= | Rob Tibshirani The History of science | Dept. of Statistics is full of fruitful errors | Sequoia Hall and barren truths | Stanford Univ | Stanford, CA Arthur Koestler | USA 94305 Phone: 1-415-725 2237 Email: tibs@playfair.stanford.edu FAX: 1-416-725-8977 From jhoh@vision.postech.ac.kr Thu May 18 09:00:40 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 18 May 95 09:00:35 -0500; AA20779 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 18 May 95 09:00:32 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa14523; 18 May 95 1:01:09 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa14521; 18 May 95 0:48:17 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa09264; 18 May 95 0:48:05 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa06438; 17 May 95 21:37:40 EDT Received: from vision.postech.ac.kr by CS.CMU.EDU id aa08987; 17 May 95 21:34:13 EDT Received: from [141.223.40.5] (tico.postech.ac.kr [141.223.40.5]) by vision.postech.ac.kr (8.6.9H1/8.6.6) with SMTP id KAA01904 for ; Thu, 18 May 1995 10:23:55 +0900 Message-Id: Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Thu, 18 May 1995 10:40:52 +0900 To: Connectionists@cs.cmu.edu From: "Prof. Jong-Hoon Oh" Subject: Statistical Physics of Neural Networks: Preprints available via ftp Dear Colleagues, Preprints to be published in the proceedings of NNSMP95 (Neural Networks: The Statistical Mechanics Perspective) are available. Here is the table of contents for NNSMP95 Proceedings. It is available via anonymous ftp at tico.postech.ac.kr in the pub/NNSMP/proceedings95. For accompanying mailing list service, please read README file. **************************************************************************** Jong-Hoon Oh Associate Professor, Department of Physics jhoh@vision.postech.ac.kr Pohang Institute of Science Technology Tel) +82-562-2792069 Hyoja San 31 Pohang, 790-784 Kyoungbuk, Korea Fax) +82-562-2793099 **************************************************************************** -----------------Table of Contents - LaTeX format -------------------------- \documentstyle[12pt]{article} \begin{document} \centerline{\Large\bf Table of Contents} \bigskip \bigskip Preface \bigskip {\bf Part I. Learning Curves} \begin{itemize} \item Statistical Theory of Learning Curves S. Amari, N. Murata and K. Ikeda. \item Generalization in Two-Layer Neural Networks J.-H. Oh, K. Kang, C. Kwon and Y. Park \item Annealed Theories of Learning H. S. Seung \item Mutual Information and Bayes Methods for Learning a Distribution D. Haussler and M. Opper \item General Bounds for Predictive Errors in Supervised Learning M. Opper and D. Haussler \item Perceptron Learning: The Largest Version Space M. Biehl and M. Opper \item Large Scale Simulations for Learning Curves K.-R. M\"uller, M. Finke, N. Murata and S. Amari \item Geometry of Admissible Parameter Region in Neural Learning K. Ikeda and S. Amari \item Learning by a Population of Perceptrons K. Kang, J.-H. Oh and C. Kwon \end{itemize} {\bf Part II. Dynamics} \begin{itemize} \item On-Line Learning of Dichotomies: Algorithms and Learning Curves H. Sompolinsky, N. Barkai and H. S. Seung \item The Bit-Generator and Time-Series Prediction E. Eisenstein, I. Kanter, D. A. Kessler and W. Kinzel \item Phase Dynamics of Two and Three Coupled Hodgkin-Huxley Neurons under DC Currents S. Kim, S. G. Lee, H. Kook and J. H. Shin \item Periodic Synchronization in Networks of Neuronal Oscillators M. Y. Choi \item Synchronization in Neural Networks with Finite Storage Capacity K. Park and M. Y. Choi \end{itemize} {\bf Part III. Associative Memory and Other Topics} \begin{itemize} \item The Cavity Method: Applications to Learning and Retrieval in Neural Networks K. Y. M. Wong \item Storage Capacity of a Fully Connected Committee Machine C. Kwon, Y. Park and J.-H. Oh \item Thermodynamic Properties of the Multi-Neuron Interaction Model without Truncating the Interaction D.~Boll\'e, J.~Huyghebaert and G.~M.~Shim \item Symmetry between Neuronal and Synaptic Dynamics of Neural Net H.-F. Yanai \item Learning and Maximum Entropy in General Boltzmann Machines C. Hicks and H. Ogawa \item On the (Free) Energy of Stochastic and Continuous Hopfield Neural Networks J. van den Berg and J. C. Bioch \item Neural Thermodynamics for Biological Ensembles A. Coster \end{itemize} {\bf Part IV. Applications} \begin{itemize} \item Learning Algorithms for Classification: A Comparison on Handwritten Digit Recognition Y. LeCun, L. D. Jackel, L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, U. A. M\"uller, E. S\"ackinger, P. Simard and V. Vapnik \item On the Consequences of the Statistical Mechanics Theory of Learning Curves for the Model Selection Problem M. J. Kearns \item Learning of a Two-Layer Neural Network with Flexible Hidden Layer Size J. Kim, K. Kang and J.-H. Oh \item Distributed Population Representation in Proprioceptive Cortex S. Cho, M. Jang and J. A. Reggia \item Designing Cost Functions for Additional Network Functionality S. Y. Lee \item Self-Organization of Gaussian Mixture Model for PDF Estimation S. Lee and S. Shimoji \end{itemize} \end{document} **************************************************************************** Jong-Hoon Oh Associate Professor, Department of Physics jhoh@vision.postech.ac.kr Pohang Institute of Science Technology Tel) +82-562-2792069 Hyoja San 31 Pohang, 790-784 Kyoungbuk, Korea Fax) +82-562-2793099 **************************************************************************** From schlimme@eecs.wsu.edu Fri May 19 07:01:28 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 19 May 95 07:01:15 -0500; AA13505 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Fri, 19 May 95 07:01:07 -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 PAA17286; Fri, 19 May 1995 15:01:29 +0800 Message-Id: <199505190701.PAA17286@cs.uwa.oz.au> From: schlimme@eecs.wsu.edu (Jeffrey C. Schlimmer) To: nl-kr@ai.sunnyside.com (comp.ai.nlang-know-rep), nac@sparky.sterling.com (news.announce.conferences), scivw-request@hitl.washington.edu (sci.virtual-worlds), colt@cs.uiuc.edu (COLT list), mlnet@csd.abdn.ac.uk (MLnet Admin), reinforce@cs.uwa.edu.au (Reinforcement List), www@sigart.acm.org (SIGART), maass@igi.tu-graz.ac.at (COLT 95), Stefan.Wrobel@gmd.de (ECML 95), weltyc@cs.vassar.edu (IJCAI 95), alan@lrdc4.lrdc.pitt.edu (COG SCI 95), ai-stats@watstat.uwaterloo.ca, ai-cbr@mailbase.ac.uk, dietmar@cognition.iig.uni-freiburg.de (European CBR Newsletter) Subject: Machine Learning Conference Travel Reimbursements Date: Thu, 18 May 1995 12:42:21 -0700 TRAVEL REIMBURSEMENTS Twelfth International Conference on Machine Learning Granlibakken Resort, Tahoe City, California, U.S.A. July 9-12, 1995 ML95 announces the availability of travel reimbursements for students not covered by the ICSI announcement and not currently supported by other funds for the conference. These reimbursements are meant to be for extremely needy students who would otherwise not be able to attend the conference. Procedure: 1. Save all receipts for lodging and transportation (airfare, trains, autos). Include a copy of student ID and letter from your advisor stating that you have no other means of support. 2. At the conference, fill out a travel reimbursement request form. 3. A few weeks after the conference we will notify you as to how much we can reimburse you. Although we cannot guarantee specific dollar amounts, we should be able to support at least 10-12 extremely needy students with $200-$300 of reimbursements. The reason we can't guarantee support is because we currently don't have all the financial information from registration. http://www.eecs.wsu.edu/~schlimme/ml95.html Jeffrey C. Schlimmer, Asst. Prof., School of EE & CS, Washington State University, Pullman, WA 99164-2752, (509) 335-2399, (509) 335-3818 FAX http://www.eecs.wsu.edu/~schlimme/ -----BEGIN PGP PUBLIC KEY BLOCK----- Version: 2.6.2 mQBvAy+obPwAAAEDALrQe0O/IrbBRuhPsWs9uvpCwBrNJBug+bqq25JFztmuHEEa jpmkHHe9Z5fnybs7o97E/ylN2riwoqm3TcK3F7idvIBsdaMZhpwHHlDnHHP0chx8 PY6hHbJk2X/kkZvF6QARAQABtCxKZWZmcmV5IEMuIFNjaGxpbW1lciA8c2NobGlt bWVAZWVjcy53c3UuZWR1Pg== =exEt -----END PGP PUBLIC KEY BLOCK----- From pihong@cse.ogi.edu Fri May 19 08:19:12 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 19 May 95 08:19:10 -0500; AA14368 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Fri, 19 May 95 08:18:56 -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 PAA17946; Fri, 19 May 1995 15:23:17 +0800 Message-Id: <199505190723.PAA17946@cs.uwa.oz.au> From: pihong@cse.ogi.edu (Hong Pi) To: Reinforce@cs.uwa.edu.au Subject: Neural Net Course (last call for participation) Date: Thu, 18 May 95 17:45 PDT Oregon Graduate Institute of Science & Technology, Office of Continuing Education, offers the short course: NEURAL NETWORKS: ALGORITHMS AND APPLICATIONS June 12-16, 1995, at the OGI campus near Portland, Oregon. Course Organizer: John E. Moody Lead Instructor: Hong Pi With Lectures By: Dan Hammerstrom Todd K. Leen John E. Moody Thorsteinn S. Rognvaldsson Eric A. Wan Artificial neural networks (ANN) have emerged as a new information processing technique and an effective computational model for solving pattern recognition and completion, feature extraction, optimization, and function approximation problems. This course introduces participants to the neural network paradigms and their applications in pattern classification; system identification; signal processing and image analysis; control engineering; diagnosis; time series prediction; financial analysis and trading; and speech recognition. Designing a neural network application involves steps from data preprocessing to network tuning and selection. This course, with many examples, application demos and hands-on lab practice, will familiarize the participants with the techniques necessary for building successful applications. About 50 percent of the class time is assigned to lab sessions. The simulations will be based on Matlab, the Matlab Neural Net Toolbox, and other software running on 486 PCs. Prerequisites: Linear algebra and calculus. Previous experience with using Matlab is helpful, but not required. Who will benefit: Technical professionals, business analysts and other individuals who wish to gain a basic understanding of the theory and algorithms of neural computation and/or are interested in applying ANN techniques to real-world, data-driven modeling problems. Course Objectives: After completing the course, students will: - Understand the basic neural networks paradigms - Be familiar with the range of ANN applications - Have a good understanding of the techniques for designing successful applications - Gain hands-on experience with ANN modeling. Course Outline Neural Networks: Biological and Artificial The biological inspiration. History of neural computing. Types of architectures and learning algorithms. Application areas. Simple Perceptrons and Adalines Decision surfaces. Perceptron and Adaline learning rules. Stochastic gradient descent. Lab experiments. Multi-Layer Feed-Forward Networks I Multi-Layer Perceptrons. Back-propagation learning. Generalization. Early Stopping. Network performance analysis. Lab experiments. Multi-Layer Feed-Forward Networks II Radial basis function networks. Projection pursuit regression. Variants of back-propagation. Levenburg-Marquardt optimization. Lab experiments. Network Performance Optimization Network pruning techniques. Input variable selection. Sensitivity Analysis. Regularization. Lab experiments. Neural Networks for Pattern Recognition and Classification Nonparametric classification. Logistic regression. Bayesian approach. Statistical inference. Relation to other classification methods. Self-Organized Networks and Unsupervised Learning K-means clustering. Kohonen feature mapping. Learning vector quantization. Adaptive principal components analysis. Exploratory projection pursuit. Applications. Lab experiments. Time Series Prediction with Neural Networks Linear time series models. Nonlinear approaches. Case studies: economic and financial time series analysis. Lab experiments. Neural Network for Adaptive Control Nonlinear modeling in control. Neural network representations for dynamical systems. Reinforcement learning. Applications. Lab Experiments. Massively Parallel Implementation of Neural Nets on the Desktop Architecture and application demos of the Adaptive Solutions' CNAPS System. Summary and Perspectives About the Instructors Dan Hammerstrom received the B.S. degree in Electrical Engineering, with distinction, from Montana State University, the M.S. degree in Electrical Engineering from Stanford University, and the Ph.D. degree in Electrical Engineering from the University of Illinois. He was on the faculty of Cornell University from 1977 to 1980 as an assistant professor. From 1980 to 1985 he worked for Intel where he participated in the development and implementation of the iAPX-432 and i960 and, as a consultant, the iWarp systolic processor that was jointly developed by Intel and Carnegie Mellon University. He is an associate professor at Oregon Graduate Institute where he is pursuing research in massively parallel VLSI architectures, and is the founder and Chief Technical Officer of Adaptive Solutions, Inc. He is the architect of the Adaptive Solutions CNAPS neurocomputer.Dr. Hammerstrom's research interests are in the area of the VLSI architectures for pattern recognition. Todd K. Leen is associate professor of Computer Science and Engineering at Oregon Graduate Institute of Science & Technology. He received his Ph.D. in theoretical Physics from the University of Wisconsin in 1982. From 1982-1987 he worked at IBM Corporation, and then pursued research in mathematical biology at Good Samaritan Hospital's Neurological Sciences Institute. He joined OGI in 1989. Dr. Leen's current research interests include neural learning, algorithms and architectures, stochastic optimization, model constraints and pruning, and neural and non-neural approaches to data representation and coding. He is particularly interested in fast, local modeling approaches, and applications to image and speech processing. Dr. Leen served as theory program chair for the 1993 Neural Information Processing Systems (NIPS) conference, and workshops chair for the 1994 NIPS conference. John E. Moody is associate professor of Computer Science and Engineering at Oregon Graduate Institute of Science & Technology. His current research focuses on neural network learning theory and algorithms in it's many manifestations. He is particularly interested in statistical learning theory, the dynamics of learning, and learning in dynamical contexts. Key application areas of his work are adaptive signal processing, adaptive control, time series analysis, forecasting, economics and finance. Moody has authored over 35 scientific papers, more than 25 of which concern the theory, algorithms, and applications of neural networks. Prior to joining the Oregon Graduate Institute, Moody was a member of the Computer Science and Neuroscience faculties at Yale University. Moody received his Ph.D. and M.A. degrees in Theoretical Physics from Princeton University, and graduated Summa Cum Laude with a B.A. in Physics from the University of Chicago. Hong Pi is a senior research associate at Oregon Graduate Institute. He received his Ph.D. in theoretical physics from University of Wisconsin. His research interests include nonlinear modeling, neural network algorithms and applications. Thorsteinn S. Rognvaldsson received the Ph.D. degree in theoretical physics from Lund University, Sweden, in 1994. His research interests are Neural Networks for prediction and classification. He is currently a postdoctoral research associate at Oregon Graduate Institute. Eric A. Wan, Assistant Professor of Electrical Engineering and Applied Physics, Oregon Graduate Institute of Science & Technology, received his Ph.D. in electrical engineering from Stanford University in 1994. His research interests include learning algorithms and architectures for neural networks and adaptive signal processing. He is particularly interested in neural applications to time series prediction, speech enhancement, system identification, and adaptive control. He is a member of IEEE, INNS, Tau Beta Pi, Sigma Xi, and Phi Beta Kappa. For a complete course brochure contact: Linda M. Pease, Director Office of Continuing Education Oregon Graduate Institute of Science & Technology PO Box 91000 Portland, OR 97291-1000 +1-503-690-1259 +1-503-690-1686 (fax) e-mail: continuinged@admin.ogi.edu WWW home page: http://www.ogi.edu ^*^*^*^*^*^*^*^*^*^*^**^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^* Linda M. Pease, Director lpease@admin.ogi.edu Office of Continuing Education Oregon Graduate Institute of Science & Technology 20000 N.W. Walker Road, Beaverton OR 97006 USA (shipping) P.O. Box 91000, Portland, OR 97291-1000 USA (mailing) +1-503-690-1259 +1-503-690-1686 fax "The future belongs to those who believe in the beauty of their dreams" -Eleanor Roosevelt ^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^* From schlimme@eecs.wsu.edu Fri May 19 08:50:01 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 19 May 95 08:49:51 -0500; AA14567 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Fri, 19 May 95 08:49:42 -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 PAA17293; Fri, 19 May 1995 15:01:56 +0800 Message-Id: <199505190701.PAA17293@cs.uwa.oz.au> From: schlimme@eecs.wsu.edu (Jeffrey C. Schlimmer) To: nl-kr@ai.sunnyside.com (comp.ai.nlang-know-rep), nac@sparky.sterling.com (news.announce.conferences), scivw-request@hitl.washington.edu (sci.virtual-worlds), colt@cs.uiuc.edu (COLT list), mlnet@csd.abdn.ac.uk (MLnet Admin), reinforce@cs.uwa.edu.au (Reinforcement List), www@sigart.acm.org (SIGART), maass@igi.tu-graz.ac.at (COLT 95), Stefan.Wrobel@gmd.de (ECML 95), weltyc@cs.vassar.edu (IJCAI 95), alan@lrdc4.lrdc.pitt.edu (COG SCI 95), ai-stats@watstat.uwaterloo.ca, ai-cbr@mailbase.ac.uk, dietmar@cognition.iig.uni-freiburg.de (European CBR Newsletter) Subject: Machine Learning Conference Schedule Date: Thu, 18 May 1995 12:52:21 -0700 SCHEDULE Twelfth International Conference on Machine Learning Granlibakken Resort, Tahoe City, California, U.S.A. July 9-12, 1995 MONDAY, JULY 10 8.45 - 9.00 Welcome address 9.00 - 10.00 Invited speaker introduced by M. Jordan David Heckerman, Microsoft Research, "Machine Learning and Uncertainty in AI" 10.00 - 10.30 Break 10.30 - 12.00 Plenary session chaired by T. Dietterich "Horizontal Generalization", David H. Wolpert (Santa Fe Institute, USA) "TD Models: Modeling the World at a Mixture of Time Scales", Richard S. Sutton (USA) "Learning Policies for Partially Observable Environments: Scaling Up", Michael L. Littman, Anthony R. Cassandra, Leslie P. Kaelbling (Brown U., USA) 12.00 - 1.30 Lunch 1.30 - 3.30 Parallel sessions Track 1 chaired by A. Moore "Optimal Adaptive Disk Spindown via Rent-to-Buy in Probabilistic Environments", P. Krishnan, Philip M. Long, Jeffrey Scott Vitter (Duke U., USA) "Free to Choose: Investigating the Sample Complexity of Active Learning of Real-Valued Functions", Partha Niyogi (Massachusetts Institute of Technology, USA) "Active Exploration and Learning in Real-Valued Spaces using Multi-Armed Bandit Allocation Indices", Marcos Salganicoff (U. of Delaware, USA), Lyle H. Ungar (U. of Pennsylvania, USA) "Q-Learning for Bandit Problems", Michael Duff (U. of Massachusetts, Amherst, USA) Track 2 chaired by W. Buntine "On Pruning and Averaging Decision Trees", Jonathan J. Oliver (Monash U., Australia) "Retrofitting Decision Tree Classifiers using Kernel Density Estimation", Padhraic Smyth, Alex Gray, Usama M. Fayyad (Jet Propulsion Laboratory, USA) "Automatic Selection of Split Criterion during Tree Growing Based on Node Location", Carla E. Brodley (Purdue U., USA) "Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves", David Lubinsky (U. of Witwatersrand, South Africa) Track 3 chaired by S. Kasif "For Every Generalization Action, Is There an Equal and Opposite Reaction?", R. Bharat Rao (Siemens Corporate Research, USA), Diana Gordon, William Spears (Naval Research Laboratory, USA) "Error-Correcting Output Coding Corrects Bias and Variance", Eun Bae Kong, Thomas G. Dietterich (Oregon State U., USA) "A Bayesian Analysis of Algorithms for Learning Finite Functions", James Cussens (Glasgow Caledonian U., Scotland) "Automatic Parameter Selection by Minimizing Estimated Error", Ron Kohavi, George H. John (Stanford U., USA) 3.30 - 4.00 Break 4.00 - 5.30 Parallel sessions Track 1 chaired by S. Mahadevan "Efficient Memory-Based Dynamic Programming", Jing Peng (U. of California, Riverside, USA) "Efficient Learning from Delayed Rewards through Symbiotic Evolution", David E. Moriarty, Risto Miikkulainen (U. of Texas at Austin, USA) "Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State", R. Andrew McCallum (U. of Rochester, USA) Track 2 chaired by U. Fayyad "Learning Prototypical Concept Descriptions", Piew Datta, Dennis Kibler (U. of California, Irvine, USA) "K*: An Instance-Based Learner Using an Entropic Distance Measure", John G. Cleary, Leonard E. Trigg (U. of Waikato, New Zealand) "Bounds on the Classification Error of the Nearest Neighbor Rule", John A. Drakopoulos (Stanford U., USA) Track 3 chaired by K. Yamanishi "A Comparison of Induction Algorithms for Selective and Non-Selective Bayesian Classifiers", Moninder Singh (U. of Pennsylvania, USA), Gregory M. Provan (Institute for Decision Systems Research, USA) "Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's", Kevin Lang (NEC Research Institute, USA) "Symbiosis in Multimodal Concept Learning", Jukka Hekanaho (Abo Akademi U., Finland) 6.00 - 7.30 Reception TUESDAY, JULY 11 8.30 - 9.30 Invited speaker introduced by L. Kaelbling Dean Pomerleau, CMU, "Machine Learning for Autonomous Driving and Collision Warning" 9.30 - 10.00 Plenary session chaired by L. Kaelbling "Explanation-Based Learning and Reinforcement Learning: A Unified View", Thomas G. Dietterich (Oregon State U., USA), Nicholas S. Flann (Utah State U., USA) 10.00 - 10.30 Break 10.30 - 12.00 Plenary session chaired by R. Greiner "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees", Peter Auer (U. of California, Santa Cruz, USA), Wolfgang Maass (T.U Graz, Austria), Robert Holte (U. of Ottawa, Canada) "Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain", Avrim Blum (Carnegie Mellon U., USA) "Fast Effective Rule Induction", William W. Cohen (AT&T Bell Laboratories, USA) 12.00 - 1.30 Lunch 1.30 - 3.30 Parallel sessions Track 1 chaired by J. Dejong "Case-Based Acquisition of Place Knowledge", Pat Langley (Institute for the Study of Learning and Expertise, USA), Karl Pfleger (Stanford U., USA) "A Case Study of Explanation-Based Control", Gerald DeJong (U. of Illinois at Urbana-Champaign, USA) "Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition", Xuemei Wang (Carnegie Mellon U., USA) "Inductive Learning of Reactive Action Models", Scott Benson (Stanford U., USA) Track 2 chaired by J. Catlett "Compression-Based Discretization of Continuous Attributes", Bernhard Pfahringer (Austrian Research Institute for AI, Austria) "MDL and Categorical Theories (Continued)", J.R. Quinlan (U. of Sydney, Australia) "Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability", Jurgen Schmidhuber (IDSIA, Lugano, Switzerland) "Inferring Reduced Ordered Decision Graphs of Minimal Description Length", Arlindo Oliveira (INESC, Lisboa, Portugal), Alberto Sangiovanni-Vincentelli (U. of California, Berkeley, USA) Track 3 chaired by R. Mooney "A Linguistically-Based Semantic Bias for Theory Revision", Clifford Brunk, Michael Pazzani (U. of California, Irvine, USA) "The Challenge of Revising an Impure Theory", Russell Greiner (Siemens Corporate Research, USA) "Lessons from Theory Revision Applied to Constructive Induction", Steven K. Donoho, Larry Rendell (U. of Illinois at Urbana-Champaign, USA) "Protein Folding: Symbolic Refinement Competes with Neural Networks", Susan Craw, Paul Hutton (Robert Gordon U., Scotland) 3.30 - 4.00 Break 4.00 - 5.30 Parallel sessions Track 1 chaired by K. Yamanishi "A Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward", Hajime Kimura, Masayuki Yamamura, Shigenobu Kobayashi (Tokyo Institute of Technology, Japan) "Fast and Efficient Reinforcement Learning with Truncated Temporal Differences", Pawel Cichosz, Jan J. Mulawka (Warsaw U. of Technology, Poland) "A Cooperative Q-Learning Approach to the Traveling Salesman Problem", Luca Maria Gambardella (IDSIA, Lugano, Switzerland), Marco Dorigo (Universite Libre de Bruxelles, Belgium) Track 2 chaired by P. Tadepalli "A Comparative Evaluation of Voting and Meta-Learning on Partitioned Data", Philip K. Chan, Salvatore J. Stolfo (Columbia U., USA) "Learning with Small Disjuncts", Gary M. Weiss (Rutgers, USA) "On Handling Tree-Structured Attributes in Decision Tree Learning", Hussein Almuallim, Yasuhiro Akiba, Shigeo Kaneda (NTT Communication Science Laboratories, Japan) Track 3 chaired by L. Hellerstein "Comparing Several Linear-Threshold Learning Algorithms on Tasks Involving Superfluous Attributes", Nick Littlestone (NEC Research Institute, USA) "Efficient Learning with Virtual Threshold Gates", Wolfgang Maass (T.U Graz, Austria), Manfred K. Warmuth (U. of California, Santa Cruz, USA) "A Quantitative Study of Hypothesis Selection", Philip W. L. Fong (U. of Waterloo, Canada) TBA Banquet at High Camp, Squaw Valley WEDNESDAY, JULY 12 8.30 - 9.30 Invited speaker introduced by C. Cardie Bruce Croft, U. Massachusetts at Amherst, "Machine Learning and Information Retrieval" 9.30 - 10.00 Plenary session chaired by R. Sutton "Removing the Genetics from the Standard Genetic Algorithm", Shumeet Baluja, Rich Caruana (Carnegie Mellon U., USA) 10.00 - 10.30 Break 10.30 - 12.00 Plenary session chaired by S. Thrun "Residual Algorithms: Reinforcement Learning with Function Approximation", Leemon Baird (US Air Force Academy, USA) "Stable Function Approximation in Dynamic Programming", Geoffrey Gordon (Carnegie Mellon U., USA) "NewsWeeder: Learning to Filter News", Ken Lang (Carnegie Mellon U., USA) 12.00 - 1.30 Lunch 1.30 - 3.00 Parallel sessions Track 1 chaired by M. Pazzani "An Inductive Learning Approach to Prognostic Prediction", W. Nick Street, O. L. Mangasarian, W. H. Wolberg (U. of Wisconsin, USA) "Distilling Reliable Information from Unreliable Theories", Sean P. Engelson, Moshe Koppel (Bar-Ilan U., Israel) "Using Multidimensional Projections to Find Relations", Eduardo Perez, Larry Rendell (U. of Illinois at Urbana-Champaign, USA) Track 2 chaired by C. Cardie "Tracking the Best Expert", Mark Herbster, Manfred K. Warmuth (U. of California, Santa Cruz, USA) "On Learning Decision Committees", Richard Nock, Olivier Gascuel (LIRMM, Montpellier, France) "Committee-Based Sampling for Training Probabilistic Classifiers", Ido Dagan, Sean P. Engelson (Bar-Ilan U., Israel) Track 3 chaired by I. Bratko "Automatic Speaker Recognition: An Application of Machine Learning", Brett Squires, Claude Sammut (U. of New South Wales, Australia) "Learning Collection Fusion Strategies for Information Retrieval", Geoffrey Towell, Ellen M. Voorhees, Narendra K. Gupta, Ben Johnson-Laird (Siemens Corporate Research, USA) "Text Categorization and Relational Learning", William W. Cohen (AT&T Bell Laboratories, USA) 3.00 - 3.30 Break 3.30 - 4.30 Parallel sessions Track 1 chaired by D. Wilkins "Learning Proof Heuristics by Adapting Parameters", Matthias Fuchs (U. Kaiserslautern, Germany) "Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network", Justine Blackmore, Risto Miikkulainen (U. of Texas at Austin, USA) Track 2 chaired by C. Schaffer "Efficient Algorithms for Finding Multi-Way Splits for Decision Trees", Thruxton Fulton, Simon Kasif, Steven Salzberg (Johns Hopkins U., USA) "Supervised and Unsupervised Discretization of Continuous Features", James Dougherty, Ron Kohavi, Mehran Sahami (Stanford U., USA) Track 3 chaired by C. Cardie "Learning Hierarchies from Ambiguous Natural Language Data", Takefumi Yamazaki (NTT Communication Science Laboratories, Japan), Michael J. Pazzani, Christopher Merz (U. of California, Irvine, USA) "On-Line Learning of Semantic Knowledge using Multi-Dimensional Weighted Majority Algorithms", Naoki Abe, Hang Li, Atsuyoshi Nakamura (NEC C&C Research Laboratories, Japan) 4.30 - 5.00 Business meeting http://www.eecs.wsu.edu/~schlimme/ml95.html Jeffrey C. Schlimmer, Asst. Prof., School of EE & CS, Washington State University, Pullman, WA 99164-2752, (509) 335-2399, (509) 335-3818 FAX http://www.eecs.wsu.edu/~schlimme/ -----BEGIN PGP PUBLIC KEY BLOCK----- Version: 2.6.2 mQBvAy+obPwAAAEDALrQe0O/IrbBRuhPsWs9uvpCwBrNJBug+bqq25JFztmuHEEa jpmkHHe9Z5fnybs7o97E/ylN2riwoqm3TcK3F7idvIBsdaMZhpwHHlDnHHP0chx8 PY6hHbJk2X/kkZvF6QARAQABtCxKZWZmcmV5IEMuIFNjaGxpbW1lciA8c2NobGlt bWVAZWVjcy53c3UuZWR1Pg== =exEt -----END PGP PUBLIC KEY BLOCK----- From jaap.murre@mrc-apu.cam.ac.uk Fri May 19 17:05:59 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 19 May 95 17:05:56 -0500; AA19576 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 19 May 95 17:05:54 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa15635; 18 May 95 19:33:37 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa15633; 18 May 95 19:16:55 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa09996; 18 May 95 19:16:18 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa28808; 17 May 95 11:39:00 EDT Received: from sirius.mrc-apu.cam.ac.uk by CS.CMU.EDU id aa03419; 17 May 95 11:37:45 EDT Received: from rigel.mrc-apu.cam.ac.uk by sirius.mrc-apu.cam.ac.uk with Sendmail (8.6.12/V050201); Wed, 17 May 1995 16:36:43 +0100 Message-Id: <199505171536.QAA26136@sirius.mrc-apu.cam.ac.uk> From: Jaap Murre Date: Wed, 17 May 1995 16:36:43 +0100 To: connectionists-request@cs.cmu.edu Subject: Brain-size neurocomputers The following paper has been added to our ftp site: Heemskerk, J.N.H., & J.M.J. Murre (submitted). Brain-size neurocomputers: analyses and simulations of neural topologies on Fractal Architectures. Submitted to the IEEE Transactions on Neural Networks. Abstract Current neurocomputers are more than 50 million times slower than the brain. Although chip speeds exceed the switching speed of biological neurons with several orders of magnitude, artificial neural networks are of a much smaller scale than real brains. The primary aim of most neurocomputer designs is speeding up neural paradigms rather than implementing large-scale neural networks. In order to simulate neural networks of brain-size, neurocomputers need to be scaled up. We here present MindShape which is a design concept for a very large-scale neurocomputer based on a hierarchical-modular or Fractal Architecture. A Fractal Architecture can be built up from two types of elements: neural processing elements (NPEs) and communication elements (CEs). Massive usage of these elements allows for both distributed calculation and distributed control. A detailed description of this machine is presented, with reference to a realized feasibility study (the BSP400, see [1][2]). Through performance analyses and simulations of data- communication, it is shown that the Fractal Architecture supports efficient implementation of structured neural networks. We finally demonstrate that physical realization of brain-size neurocomputers is feasible with current technology. Files can be found at: ftp://ftp.mrc-apu.cam.ac.uk/pub/nn/murre/lsize.ps (1589 Kb) ftp://ftp.mrc-apu.cam.ac.uk/pub/nn/murre/lsize.ps.Z (347 Kb) ftp://ftp.mrc-apu.cam.ac.uk/pub/nn/murre/lsize.zip (495 Kb) Jan N.H. Heemskerk hmskerk@rulfsw.leidenuniv.nl Jacob M.J. Murre jaap.murre@mrc-apu.cam.ac.uk (after 1 June 1995: pn_murre@macmail.psy.uva.nl) From pihong@cse.ogi.edu Fri May 19 17:06:01 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Fri, 19 May 95 17:05:58 -0500; AA19582 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Fri, 19 May 95 17:05:56 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa15963; 19 May 95 2:56:28 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa15961; 19 May 95 2:41:18 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa10198; 19 May 95 2:40:35 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa21781; 18 May 95 20:42:53 EDT Received: from anago.cse.ogi.edu by RI.CMU.EDU id aa00785; 18 May 95 20:42:35 EDT Received: by anago.cse.ogi.edu (Smail3.1.29.1 #4) id m0sCG9A-0001TVC; Thu, 18 May 95 17:42 PDT Message-Id: Date: Thu, 18 May 95 17:42 PDT From: Hong Pi To: connectionists@cs.cmu.edu Subject: Neural net short course at OGI Oregon Graduate Institute of Science & Technology, Office of Continuing Education, offers the short course: NEURAL NETWORKS: ALGORITHMS AND APPLICATIONS June 12-16, 1995, at the OGI campus near Portland, Oregon. Course Organizer: John E. Moody Lead Instructor: Hong Pi With Lectures By: Dan Hammerstrom Todd K. Leen John E. Moody Thorsteinn S. Rognvaldsson Eric A. Wan Artificial neural networks (ANN) have emerged as a new information processing technique and an effective computational model for solving pattern recognition and completion, feature extraction, optimization, and function approximation problems. This course introduces participants to the neural network paradigms and their applications in pattern classification; system identification; signal processing and image analysis; control engineering; diagnosis; time series prediction; financial analysis and trading; and speech recognition. Designing a neural network application involves steps from data preprocessing to network tuning and selection. This course, with many examples, application demos and hands-on lab practice, will familiarize the participants with the techniques necessary for building successful applications. About 50 percent of the class time is assigned to lab sessions. The simulations will be based on Matlab, the Matlab Neural Net Toolbox, and other software running on 486 PCs. Prerequisites: Linear algebra and calculus. Previous experience with using Matlab is helpful, but not required. Who will benefit: Technical professionals, business analysts and other individuals who wish to gain a basic understanding of the theory and algorithms of neural computation and/or are interested in applying ANN techniques to real-world, data-driven modeling problems. Course Objectives: After completing the course, students will: - Understand the basic neural networks paradigms - Be familiar with the range of ANN applications - Have a good understanding of the techniques for designing successful applications - Gain hands-on experience with ANN modeling. Course Outline Neural Networks: Biological and Artificial The biological inspiration. History of neural computing. Types of architectures and learning algorithms. Application areas. Simple Perceptrons and Adalines Decision surfaces. Perceptron and Adaline learning rules. Stochastic gradient descent. Lab experiments. Multi-Layer Feed-Forward Networks I Multi-Layer Perceptrons. Back-propagation learning. Generalization. Early Stopping. Network performance analysis. Lab experiments. Multi-Layer Feed-Forward Networks II Radial basis function networks. Projection pursuit regression. Variants of back-propagation. Levenburg-Marquardt optimization. Lab experiments. Network Performance Optimization Network pruning techniques. Input variable selection. Sensitivity Analysis. Regularization. Lab experiments. Neural Networks for Pattern Recognition and Classification Nonparametric classification. Logistic regression. Bayesian approach. Statistical inference. Relation to other classification methods. Self-Organized Networks and Unsupervised Learning K-means clustering. Kohonen feature mapping. Learning vector quantization. Adaptive principal components analysis. Exploratory projection pursuit. Applications. Lab experiments. Time Series Prediction with Neural Networks Linear time series models. Nonlinear approaches. Case studies: economic and financial time series analysis. Lab experiments. Neural Network for Adaptive Control Nonlinear modeling in control. Neural network representations for dynamical systems. Reinforcement learning. Applications. Lab Experiments. Massively Parallel Implementation of Neural Nets on the Desktop Architecture and application demos of the Adaptive Solutions' CNAPS System. Summary and Perspectives About the Instructors Dan Hammerstrom received the B.S. degree in Electrical Engineering, with distinction, from Montana State University, the M.S. degree in Electrical Engineering from Stanford University, and the Ph.D. degree in Electrical Engineering from the University of Illinois. He was on the faculty of Cornell University from 1977 to 1980 as an assistant professor. From 1980 to 1985 he worked for Intel where he participated in the development and implementation of the iAPX-432 and i960 and, as a consultant, the iWarp systolic processor that was jointly developed by Intel and Carnegie Mellon University. He is an associate professor at Oregon Graduate Institute where he is pursuing research in massively parallel VLSI architectures, and is the founder and Chief Technical Officer of Adaptive Solutions, Inc. He is the architect of the Adaptive Solutions CNAPS neurocomputer.Dr. Hammerstrom's research interests are in the area of the VLSI architectures for pattern recognition. Todd K. Leen is associate professor of Computer Science and Engineering at Oregon Graduate Institute of Science & Technology. He received his Ph.D. in theoretical Physics from the University of Wisconsin in 1982. From 1982-1987 he worked at IBM Corporation, and then pursued research in mathematical biology at Good Samaritan Hospital's Neurological Sciences Institute. He joined OGI in 1989. Dr. Leen's current research interests include neural learning, algorithms and architectures, stochastic optimization, model constraints and pruning, and neural and non-neural approaches to data representation and coding. He is particularly interested in fast, local modeling approaches, and applications to image and speech processing. Dr. Leen served as theory program chair for the 1993 Neural Information Processing Systems (NIPS) conference, and workshops chair for the 1994 NIPS conference. John E. Moody is associate professor of Computer Science and Engineering at Oregon Graduate Institute of Science & Technology. His current research focuses on neural network learning theory and algorithms in it's many manifestations. He is particularly interested in statistical learning theory, the dynamics of learning, and learning in dynamical contexts. Key application areas of his work are adaptive signal processing, adaptive control, time series analysis, forecasting, economics and finance. Moody has authored over 35 scientific papers, more than 25 of which concern the theory, algorithms, and applications of neural networks. Prior to joining the Oregon Graduate Institute, Moody was a member of the Computer Science and Neuroscience faculties at Yale University. Moody received his Ph.D. and M.A. degrees in Theoretical Physics from Princeton University, and graduated Summa Cum Laude with a B.A. in Physics from the University of Chicago. Hong Pi is a senior research associate at Oregon Graduate Institute. He received his Ph.D. in theoretical physics from University of Wisconsin. His research interests include nonlinear modeling, neural network algorithms and applications. Thorsteinn S. Rognvaldsson received the Ph.D. degree in theoretical physics from Lund University, Sweden, in 1994. His research interests are Neural Networks for prediction and classification. He is currently a postdoctoral research associate at Oregon Graduate Institute. Eric A. Wan, Assistant Professor of Electrical Engineering and Applied Physics, Oregon Graduate Institute of Science & Technology, received his Ph.D. in electrical engineering from Stanford University in 1994. His research interests include learning algorithms and architectures for neural networks and adaptive signal processing. He is particularly interested in neural applications to time series prediction, speech enhancement, system identification, and adaptive control. He is a member of IEEE, INNS, Tau Beta Pi, Sigma Xi, and Phi Beta Kappa. For a complete course brochure contact: Linda M. Pease, Director Office of Continuing Education Oregon Graduate Institute of Science & Technology PO Box 91000 Portland, OR 97291-1000 +1-503-690-1259 +1-503-690-1686 (fax) e-mail: continuinged@admin.ogi.edu WWW home page: http://www.ogi.edu From patrick@magi.ncsl.nist.gov Sat May 20 00:45:36 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Sat, 20 May 95 00:45:34 -0500; AA22437 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Sat, 20 May 95 00:45:33 -0500 Received: by TELNET-1.SRV.CS.CMU.EDU id aa16695; 19 May 95 17:08:50 EDT Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa16673; 19 May 95 16:42:03 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa16671; 19 May 95 16:27:34 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa10790; 19 May 95 16:27:14 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa28572; 19 May 95 9:45:56 EDT Received: from MAGI.NCSL.NIST.GOV by CS.CMU.EDU id aa21626; 19 May 95 9:44:29 EDT Received: from ickaos.ncsl.nist.gov by magi.ncsl.nist.gov (4.1/NIST) id AA24500; Fri, 19 May 95 09:43:52 EDT Date: Fri, 19 May 95 09:43:52 EDT From: Patrick Grother Organization: National Institute of Standards and Technology, Gaithersburg, MD Message-Id: <9505191343.AA24500@magi.ncsl.nist.gov> To: doc-own@cfar.umd.edu, connectionists@cs.cmu.edu Subject: New NIST Technical Document Image Database NIST Special Database 20 Scientific and Technical Document Database Special Database 20 contains 23468 high resolution binary images obtained from copyright-expired scientific and technical journals and books. The images contain a very rich set of graphic elements such as graphs, tables, equations, two column text, maps, pictues, footnotes, annotations, and arrays of such elements. No ground truthing or original typesetting information is available. The images contain predominantly machine printed English, although three French and German documents are included. + 104 articles, books, journals + 23468 full page binary images + High Resolution 15.75 dots per mm ( 400 dpi ) + 4 compact discs each containing about 500 Mb + Updated CCITT IV Compression Source Code: 25x compression + A structural statistics file for each image + Page rotation estimates + Software utilities Special Database 20 is available as a four 5.25 inch CD-ROM set in the ISO-9660 format. Price: $1000.00 US. For sales contact: Standard Reference Data National Institute of Standards and Technology Building 221, Room A323 Gaithersburg, MD 20899 Voice: (301) 975-2208 FAX: (301) 926-0416 email: srdata@enh.nist.gov For technical details contact: Patrick Grother Visual Image Processing Group National Institute of Standards and Technology Building 225, Room A216 Gaithersburg, Maryland 20899 Voice: (301) 975-4157 email: patrick@magi.ncsl.nist.gov