From bricolo@sissa.it Sat Mar 1 01:42:07 1997 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id BAA13314 for ; Sat, 1 Mar 1997 01:42:01 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.7.6/8.7.3) with SMTP id BAA17348 for ; Sat, 1 Mar 1997 01:41:59 -0600 (CST) Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa13239; 28 Feb 97 17:25:28 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa13236; 28 Feb 97 17:14:12 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa13106; 28 Feb 97 17:13:29 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa23803; 28 Feb 97 11:15:41 EST Received: from [147.122.1.17] by CS.CMU.EDU id aa15130; 28 Feb 97 11:15:05 EST Received: (from bricolo@localhost) by shannon.sissa.it (8.8.2/8.8.2) id RAA68803 for connectionists@cs.cmu.edu; Fri, 28 Feb 1997 17:10:26 +0100 From: Emanuela Bricolo Message-Id: <199702281610.RAA68803@shannon.sissa.it> Subject: pre and postdoc positions To: connectionists@cs.cmu.edu Date: Fri, 28 Feb 1997 17:10:25 +22310718 (MET) X-Mailer: ELM [version 2.4 PL21] Content-Type: text Neuroscience at S.I.S.S.A. S.I.S.S.A., the International School for Advanced Studies of Trieste, Italy, is an interdisciplinary postgraduate research institute directly supported by the Italian Ministry for Universities and Research. Its working language is English and non-Italian Ph.D. students, postdocs and faculty comprise about a third of the total. There are no fees; fellowships are offered to all students. A new sector, Cognitive Neuroscience, has been operating within S.I.S.S.A. since November, 1995. It currently includes groups working on: * Neuropsychology and modelling of semantic memory, word processing and frontal lobe processing (Tim Shallice, coordinator of the sector) * Neurophysiology and neuropsychology of spatial representation and spatial behaviour (P.Paolo Battaglini, U. of Trieste) * Information processing and plasticity of representations in the somatosensory system (Mathew Diamond) * Neurophysiology and neurobiology of developmental and adult plasticity in the visual system (Luciano Domenici) * Information-theoretic analysis and modelling of coding in higher sensory and memory areas (Alessandro Treves) and, with the Biophysics sector * Computational neuroscience (Vincent Torre) The sector is now seeking: ** 2 postdoctoral researchers (deadline for applications March 31, 1997) ** ** up to 4 qualified non-Italian Ph.D. students (deadline April 30, 1997) * One postdoc position is for the lab of M. Diamond; the other has not been allocated. Both are for 1 year, renewable for a second year, and start in September. The spring preselection of Ph.D. students allows outstanding foreign candidates to be admitted without taking the normal entrance exam in October, in Trieste. Such students then come directly at the beginning of the regular 3-year Ph.D. program, in November. Applications for both types of position should include a full CV, stating date of birth, citizenship, proficiency in english and complete academic records; at least one letter of reference, sent directly to SISSA; and any other item useful in assessing the candidate. Applicants for Ph.D.s should be under 30, and be able to provide hardcopy certificates of their records. SISSA - Cognitive Neuroscience - via Beirut 2/4, 34013 Trieste, Italy. A local homepage can be accessed at http://www.sissa.it/ and further information obtained from the Secretariat at radikkio@sissa.it, from A. Treves at ale@limbo.sissa.it or +39-40-3787426, or from M. Diamond at diamond@sissa.it, tel +39-40-3787236. From john@dcs.rhbnc.ac.uk Sat Mar 1 01:42:25 1997 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id BAA13316 for ; Sat, 1 Mar 1997 01:42:02 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.7.6/8.7.3) with SMTP id BAA17350 for ; Sat, 1 Mar 1997 01:42:01 -0600 (CST) Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa13264; 28 Feb 97 17:35:08 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa13241; 28 Feb 97 17:16:00 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa13117; 28 Feb 97 17:14:41 EST Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa25905; 28 Feb 97 13:09:29 EST Received: from [134.219.44.52] by CS.CMU.EDU id aa16576; 28 Feb 97 13:08:26 EST Received: from localhost (localhost [127.0.0.1]) by platon.cs.rhbnc.ac.uk (8.6.9/8.6.9) with SMTP id RAA08139 ; Fri, 28 Feb 1997 17:53:56 GMT From: John Shawe-Taylor Message-Id: <199702281753.RAA08139@platon.cs.rhbnc.ac.uk> X-Authentication-Warning: platon.cs.rhbnc.ac.uk: Host localhost didn't use HELO protocol To: john@dcs.rhbnc.ac.uk, vovk@dcs.rhbnc.ac.uk, alex@dcs.rhbnc.ac.uk, pete@dcs.rhbnc.ac.uk, dave@dcs.rhbnc.ac.uk, jon@dcs.rhbnc.ac.uk, m.anthony@lse.ac.uk, Paul.Vitanyi@cwi.nl, Esko Ukkonen , orponen@igi.tu-graz.ac.at, Michel.Cosnard@lip.ens-lyon.fr, maass@igi.tu-graz.ac.at, cesabian@dsi.unimi.it, mauri , vigna@dsi.unimi.it, Felipe Cucker , Veronique.Bruyere@umh.ac.be, Christian.Michaux@umh.ac.be, Maurice.Boffa@umh.ac.be, meer@rwth-aachen.de, gavalda@lsi.upc.es, balqui@lsi.upc.es, torras@ic.upc.es, bruf@igi.tu-graz.ac.at, jpd@pip.fpms.ac.be, panizza@cse.ucsc.edu, ferretti@dsi.unimi.it, g.r.brightwell@lse.ac.uk, hpaugam@lip.ens-lyon.fr, mschmitt@igi.tu-graz.ac.at, boldi@dsi.unimi.it, Bernard.Girau@lip.ens-lyon.fr, Tapio.Elomaa@cs.Helsinki.FI, jkivinen@varisluoto.cs.Helsinki.FI, Petri.Myllymaki@cs.Helsinki.FI, koiran@lip.ens-lyon.fr, pauer@igi.tu-graz.ac.at, Didier.Puzenat@lip.ens-lyon.fr, Richard.Baron@lip.ens-lyon.fr, castro@lsi.upc.es, buhrman@cwi.nl, david@goliat.upc.es, vlavin@lsi.upc.es, carlos@goliat.upc.es, pdg@cwi.nl, N.L.Biggs@lse.ac.uk, Herman.Ehrenburg@cwi.nl, gegout@clipper.ens.fr, Olivier.Bournez@lip.ens-lyon.fr, Jean-Sylvestre.Gakwaya@umh.ac.be, simon@nereus.informatik.uni-dortmund.de, shai@csa.CS.Technion.AC.IL, bartlett@deakin.anu.edu.au, williams@faceng.anu.edu.au, lugosi@upf.es, colt@cs.uiuc.edu, Connectionists@cs.cmu.edu, enns-list@dcs.kcl.ac.uk, neur-sci@dl.ac.uk, comp-neuro@smaug.bbb.caltech.edu, neuron-request@cattell.psych.upenn.edu Subject: Technical Report Series in Neural and Computational Learning Date: Fri, 28 Feb 97 17:53:56 +0000 X-Mts: smtp The European Community ESPRIT Working Group in Neural and Computational Learning Theory (NeuroCOLT) has produced a set of new Technical Reports available from the remote ftp site described below. They cover topics in real valued complexity theory, computational learning theory, and analysis of the computational power of continuous neural networks. Abstracts are included for the titles. ----------------------------------------- NeuroCOLT Technical Report NC-TR-96-049-errata: ----------------------------------------- An errata sheet (nc-tr-96-049-errata) has been added for this previously announced paper: Extended Grzegorczyk Hierarchy in the BSS Model of Computability by Jean-Sylvestre Gakwaya, Universit\'e de Mons-Hainaut, Belgium Abstract: In this paper, we give an extension of the Grzegorczyk Hierarchy to the BSS theory of computability which is a generalization of the classical theory. We adapt some classical results related to the Grzegorczyk hierarchy in the new setting. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-001: ----------------------------------------- Multilayer neural networks: one or two hidden layers? by G. Brightwell, LSE, UK C. Kenyon, H. Paugam-Moisy, ENS Lyon, France Abstract: We study the number of hidden layers required by a multilayer neural network with threshold units to compute a function $f$ from ${\cal R}^d$ to $\{ 0,1 \}$. In dimension $d=2$, Gibson characterized the functions computable with just one hidden layer, under the assumption that there is no ``multiple intersection point" and that $f$ is only defined on a compact set. We consider the restriction of $f$ to the neighborhood of a multiple intersection point or of infinity, and give necessary and sufficient conditions for it to be locally computable with one hidden layer. We show that adding these conditions to Gibson's assumptions is not sufficient to ensure global computability with one hidden layer, by exhibiting a new non-local configuration, the ``critical cycle", which implies that $f$ is not computable with one hidden layer. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-002: ----------------------------------------- Size of multilayer networks for exact learning: analytic approach by A. Elisseeff and H. Paugam-Moisy, ENS Lyon, France Abstract: This article presents a new result about the size of a multilayer neural network computing real outputs for exact learning of a finite set of real samples. The architecture of the network is feedforward, with one hidden layer and several outputs. Starting from a fixed training set, we consider the network as a function of its weights. We derive, for a wide family of transfer functions, a lower and an upper bound on the number of hidden units for exact learning, given the size of the dataset and the dimensions of the input and output spaces. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-003: ----------------------------------------- Constructing Bayesian finite mixture models by the EM algorithm by Petri Kontkanen, Petri Myllym\"aki, Henry Tirri, University of Helsinki, Finland Abstract: In this paper we explore the use of finite mixture models for builing decision support systems capable of sound probabilistic inference. Finite mixture models have many appealing properties: they are computationally efficient in the prediction (reasoning) phase, they are universal in the sense that they can approximate any problem domain distribution, and they can handle multimodality well. We present a formulation of the model construction problem in the Bayesian framework for finite mixture models, and describe how Bayesian inference is performed given such a model. The model construction problem can be seen as missing data estimation and we describe a realization of the Expectation-Maximization (EM) algorithm for finding good models. To prove the feasibility of our approach, we report crossvalidated empirical results on several publicly available classification problem datasets, and compare our results to corresponding results obtained by alternative techniques, such as neural networks and decision trees. The comparision is based on the best results reported in the literature on the datasets in question. It appears that using the theoretically sound Bayesian framework suggested here the other reported results can be outperformed with a relatively small effort. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-004: ----------------------------------------- Comparing Predictive Inference Methods for Discrete Domains by Petri Kontkanen, Petri Myllym\"aki, Tom Silander, Henry Tirri, University of Helsinki, Finland Peter Grunwald, CWI, Amsterdam, The Netherlands Abstract: Predictive inference is seen here as the process of determining the predictive distribution of a discrete variable, given a data set of training examples and the values for the other problem domain variables. We consider three approaches for computing this predictive distribution, and assume that the joint probability distribution for the variables belongs to a set of distributions determined by a set of parametric models. In the simplest case, the predictive distribution is computed by using the model with the {\em maximum a posteriori (MAP)} posterior probability. In the {\em evidence} approach, the predictive distribution is obtained by averaging over all the individual models in the model family. In the third case, we define the predictive distribution by using Rissanen's new definition of {\em stochastic complexity}. Our experiments performed with the family of Naive Bayes models suggest that when using all the data available, the stochastic complexity approach produces the most accurate predictions in the log-score sense. However, when the amount of available training data is decreased, the evidence approach clearly outperforms the two other approaches. The MAP predictive distribution is clearly inferior in the log-score sense to the two more sophisticated approaches, but for the 0/1-score the MAP approach may still in some cases produce the best results. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-005: ----------------------------------------- Genetic Fitness Optimization Using Rapidly Mixing Markov Chains by Paul Vit\'anyi, CWI, Amsterdam, The Netherlands Abstract: A notion of highly probable fitness optimization through evolutionary computing runs on small size populations in a very general setting is proposed. This has applications to evolutionary learning. Based on rapidly mixing Markov chains, the approach pertains to most types of evolutionary genetic algorithms, genetic programming and the like. For systems having associated rapidly mixing Markov chains and appropriate stationary distributions the new method finds optimal programs (individuals) with probability almost 1. Algorithmically, the novel approach prescribes a strategy of executing many short computation runs, rather than one long computation run. Given an arbitrary evolutionary program it may be infeasible to determine whether its associated matrix is rapidly mixing. In our proposed structured evolutionary program discipline, the development of the program and the guaranty of the rapidly mixing property go hand in hand. We conclude with a tentative toy example. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-006: ----------------------------------------- On the Well-Behavedness of Important Attribute Evaluation Functions by Tapio Elomaa, University of Helsinki, Finland Juho Rousu, VTT Biotechnology and Food Research, Finland Abstract: The class of {\em well-behaved} evaluation functions simplifies and makes efficient the handling of numerical attributes; for them it suffices to concentrate on the {\em\bp s} in searching for the optimal partition. This holds always for binary partitions and also for multisplits if only the function is {\em cumulative} in addition to being well-behaved. The class of well-behaved evaluation functions is a proper superclass of convex evaluation functions. Thus, it is clear that a large proportion of the most important attribute evaluation functions are well-behaved. This paper explores the extent and boundaries of well-behaved functions. In particular, we examine the convexity and well-behavedness of C4.5's default attribute evaluation function {\em gain ratio}, which has been known to have problems with numerical attributes. Our empirical experiments show that a very simple cumulative rectification to the poor bias of {\em information gain} significantly outperforms gain ratio. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-007: ----------------------------------------- A Note on Non-complete Problems in $NP_{\Re}$ by S. Ben-David, Technion, Israel K. Meer, RWTH, Aachen, Germany C. Michaux, Universite de Mons-Hainaut, Belgium Abstract: This note deals with the structure of the class $NP_{\Re}$ introduced by Blum, Shub and Smale \cite{BSS}. It is shown that, assuming $NP_{\Re} \not\subseteq $P_{\Re} /poly$, there exists a problem in $NP_{\Re} \setminus (P_{\Re} /poly)$ which is not $NP_{\Re}$-complete (w.r.t $P_{\Re} /poly$ reductions). It also clarifies the scope of padding technique used in a former similar result by Ladner \cite{La} concerning the structure of the class $NP$ (in the common, Turing machine, model). ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-008: ----------------------------------------- Semi-algebraic Complexity -- Additive Complexity of Matrix Computational Tasks by T. Lickteig, Universit\"at Bonn, Germany K. Meer, RWTH, Aachen, Germany Abstract: This paper is devoted to the study of lower bounds on the inherent number of additions and subtractions necessary to solve some natural matrix computational tasks such as for instance computing the nullspace, a band transformation or a triangulation of a given $m \times m$ matrix. The additive complexities of such tasks are shown to grow asymptotically lik that of $m \times m$ matrix multiplication. We also propose a formalization of semi-algebraic computational tasks. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-009: ----------------------------------------- Multilayer Perceptrons and Learning by Alberto Bertoni, Paola Campadelli, Nicol\`o Cesa-Bianchi Universit\`a degli Studi di Milan, Italy Abstract: In this paper we present a survey on some interesting contributions offered by theoretical computer science to the area of supervised learning. In the first part, we discuss the computing capabilities of multilayer preceptrons with binary inputs and outputs and we describe design techniques for some classes of simple boolean functions. Finally, we show the application of communication complexity to obtain separations between complexity classes related to multilayer preceptrons. In the second part, we look at the learnability of these computing models within the PAC learning framework and some of its variants. The hardness of polynomial time prediction for the class of multilayer perceptrons is shown under cryptographical assumptions. We conclude by presenting a recently developed technique for boosting the accuracy of PAC learning algorithms. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-010: ----------------------------------------- On Bayes Methods for On-line Boolean Prediction by Nicol\`o Cesa-Bianchi, Universit\`a degli Studi di Milan, Italy David Helmbold, University of California, Santa Cruz, USA Sandra Panizza, Universit\`a degli Studi di Milan, Italy Abstract: This paper proposes a general framework, based on weighting schemes, within which the Bayes method applied to on-line Boolean prediction can be studied. By applying standard tools in Bayes theory we propose an improved variant of the Weighted Majority algorithm for deterministic prediction. The mistake bound of our variant is asymptotically equal to the mistake bound of Weighted Majority when the latter has additional side information to optimally tune its update factor. We also show general bounds on the number of prediction mistakes made by conservative versions of Bayesian algorithms. Specific instances of our bounds match bounds previously shown for different on-line prediction algorithms proposed in the past. Finally, we study a generalization of these methods to randomized predictions. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-011: ----------------------------------------- Randomized Hypotheses and Minimum Disagreement Hypotheses by Nicol\`o Cesa-Bianchi, Universit\`a degli Studi di Milan, Italy Paul Fischer, Universit\"at Dortmund, Germany Eli Shamir, Hebrew University, Israel Hans Ulrich Simon, Universit\"at Dortmund, Germany Abstract: In this paper we prove various results about PAC learning in the presence of malicious and random classification noise. Our main theme is the use of randomized hypotheses for learning with small sample sizes and high malicious noise rates. We show an algorithm that PAC learns any target class of VC-dimension $d$ using randomized hypotheses and order of $d/\ve$ training examples (up to logarithmic factors) while tolerating malicious noise rates even slightly larger than the information-theoretic bound $\ve/(1+\ve)$ for deterministic hypotheses. Combined with previous results, this implies that a lower bound $d/\Delta + \ve/\Delta^2$ on the sample size, where $\eta = \ve/(1+\ve)-\Delta$ is the malicious noise rate, applies only when using deterministic hypotheses. We then show that the information-theoretic upper bound on the noise rate for deterministic hypotheses can be replaced by $2\ve/(1+2\ve)$ if randomized hypotheses are used. Investigating further the use of randomized hypotheses, we show a strategy for learning the powerset of $d$ elements using an optimal sample size of order $d\ve/\Delta^2$ (up to logarithmic factors) and tolerating a noise rate $\eta = 2\ve/(1+2\ve)-\Delta$. We complement this result by proving that this sample size is also necessary for any class $\cC$ of VC-dimension $d$. We then discuss the performance of the minimum disagreement strategy under both malicious and random classification noise models. For malicious noise we show an algorithm that, using deterministic hypotheses, learns unions of $d$ intervals on the continuous domain $[0,1)$ using a sample size significantly smaller than that needed by the minimum disagreement strategy. For classification noise we show, generalizing a result by Laird, that order of $d/(\ve\Delta^2)$ training examples suffice (up to logarithmic factors) to learn by minimizing disagreements any target class of VC-dimension $d$ tolerating random classification noise rate $\eta = 1/2 - \Delta$. Using a lower bound by Simon, we also prove that this sample size bound cannot be significantly improved. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-012: ----------------------------------------- Learning with Restricted Focus of Attention by Shai Ben-David, Technion, Israel Eli Dichterman, LSE and RHUL, University of London, UK Abstract: We consider learning tasks in which the learner faces restrictions on the amount of information he can extract from each example he encounters. We introduce a formal framework for the analysis of such scenarios. While being a natural refinement of the PAC learning model, some of the fundamental PAC-learning results and techniques fail in the RFA paradigm; learnability in the RFA model is no longer characterized by the VC dimension, and many PAC learning algorithms are not applicable in the RFA setting. Hence, the RFA formulation reflects the need for new techniques and tools to cope with some fundamental constraints of realistic learning problems. In this work we also present some paradigms and algorithms that may serve as a first step towards answering this need. Two main types of restrictions are considered here: In the stronger one, called $k$-RFA, only $k$ of the $n$ attributes of each example are revealed to the learner, while in the weakest one, called $k$-wRFA, the restriction is made on the size of each observation ($k$ bits), and no restriction is made on how the observations are extracted from the examples. For the stronger $k$-RFA restriction we develop a general technique for composing efficient $k$-RFA algorithms, and apply it to deduce, for instance, the efficient $k$-RFA learnability of $k$-DNF formulas, and the efficient $1$-RFA learnability of axis-aligned rectangles in the Euclidean space $\R^n$. We also prove the $k$-RFA learnability of richer classes of Boolean functions (such as $k$-decision lists) with respect to a given distribution, and the efficient $(n-1)$-RFA learnability (for fixed $n$), under product distributions, of classes of subsets of $\R^n$ which are defined by mild surfaces. For the weaker $k$-wRFA restriction, we show that for $k=O(\log n)$, efficient $k$-wRFA learning is robust against classification noise. As a straightforward application, we construct a new simple noise-tolerant algorithm for the class of $k$-decision lists by constructing an intuitive $k$-wRFA algorithm for this task. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-013: ----------------------------------------- A PAC Analysis of a Bayesian Estimator by John Shawe-Taylor, Royal Holloway, University of London, UK Robert Williamson, Australian National University, Australia Abstract: Bayesian analysis of generalization can place a prior distribution on the hypotheses and estimate the volume of this space that is consistent with the training data. The larger this volume the greater the confidence in the classifier obtained. The key feature of such estimators is that they provide a posteriori estimates of generalization based on properties of the hypothesis and the training data. This contrasts with a `classical' PAC analysis which provides only a priori (worst case) bounds. Following earlier results showing that Data-sensitive analysis of generalization in the PAC sense is possible, the paper uses the techniques to give the first PAC style analysis of a Bayesian inspired estimator of generalization. The estimator concerned is the size of a ball which can be placed in the consistent region of parameter space. The ball gives a lower bound on the volume of parameter space consistent with the training set. The larger the ball the better the bound on the generalization obtained. In all cases the bounds are of good generalization with high confidence, hence bounding the tail of the distribution of generalization errors that might occur. The resulting bounds are independent of the complexity of the function class though they depend weakly on the dimensionality of the parameter space. ----------------------------------------- NeuroCOLT Technical Report NC-TR-97-014: ----------------------------------------- A New Incremental Learning Technique by Nick Dunkin, John Shawe-Taylor, Royal Holloway, University of London, UK Pascal Koiran, LIP ENS Lyon, France Abstract: We present a new type of constructive algorithm for incremental learning. The algorithm overcomes many of the problems associated with standard back propagation such as speed and optimum network size. We investigate the ability of the network to learn and test the resulting generalisation of the network. --------------------------------------------------------------------- ***************** ACCESS INSTRUCTIONS ****************** The Report NC-TR-97-001 can be accessed and printed as follows % ftp ftp.dcs.rhbnc.ac.uk (134.219.96.1) Name: anonymous password: your full email address ftp> cd pub/neurocolt/tech_reports ftp> binary ftp> get nc-tr-97-001.ps.Z ftp> bye % zcat nc-tr-97-001.ps.Z | lpr -l Similarly for the other technical reports. Uncompressed versions of the postscript files have also been left for anyone not having an uncompress facility. In some cases there are two files available, for example, nc-tr-97-002-title.ps.Z nc-tr-97-002-body.ps.Z The first contains the title page while the second contains the body of the report. The single command, ftp> mget nc-tr-97-002* will prompt you for the files you require. A full list of the currently available Technical Reports in the Series is held in a file `abstracts' in the same directory. The files may also be accessed via WWW starting from the NeuroCOLT homepage: http://www.dcs.rhbnc.ac.uk/research/compint/neurocolt or directly to the archive: ftp://ftp.dcs.rhbnc.ac.uk/pub/neurocolt/tech_reports Best wishes John Shawe-Taylor From hexmoor@cs.Buffalo.EDU Sat Mar 1 18:24:58 1997 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id SAA20929 for ; Sat, 1 Mar 1997 18:24:49 -0600 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.7.6/8.7.3) with SMTP id SAA28329 for ; Sat, 1 Mar 1997 18:24:48 -0600 (CST) Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa15437; 1 Mar 97 17:27:27 EST Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa15435; 1 Mar 97 17:15:15 EST Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa14228; 1 Mar 97 17:15:10 EST Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa16647; 1 Mar 97 12:16:09 EST Received: from hadar.cs.Buffalo.EDU by RI.CMU.EDU id aa09068; 1 Mar 97 12:15:22 EST Received: (hexmoor@localhost) by hadar.cs.Buffalo.EDU (8.8.5/8.6.4) id MAA14745 for connectionists@CS.CMU.EDU; Sat, 1 Mar 1997 12:15:20 -0500 (EST) Date: Sat, 1 Mar 1997 12:15:20 -0500 (EST) From: Henry H Hexmoor Message-Id: <199703011715.MAA14745@hadar.cs.Buffalo.EDU> To: connectionists@cs.cmu.edu Subject: CFP Dear Colleague, Maja Mataric and I are guest editing a special issue of Prof. George Bekey's Autonomous Robots Journal. Please review our CFP which is salo available online. http://www.cs.buffalo.edu/~hexmoor/autonomous-robots.html Call for papers Autonomous Robots Journal Special Issue on Learning in Autonomous Robots Guest editors: Henry Hexmoor and Maja Mataric Submission Deadline: August 15, 1997 Autonomous Robots is an international journal published by Kluwer Academic Publishers, Editor-in-Chief: George Bekey Current applications of machine learning in robotics explore learning behaviors such as obstacle avoidance, navigation, gaze control, pick and place operations, manipulating everyday objects, walking, foraging, herding, and delivering objects. It is hoped that these are first steps toward robots that will learn to perform complex operations ranging from folding clothes, cleaning up toxic waste and oil spills, picking up after the children, de-mining, look after a summer house, imitating a human teacher, or overseeing a factory or a space mission. As builders of autonomous embedded agents, researchers in robot learning deal with learning schemes in the context of physical embodiment. Strides are being made to design programs that change their initial encoding of know-how to include new concepts as well as improvements in the associations of sensing to acting. Driven by concerns about the quality and quantity of training data and real-time issues such as sparse and low-quality feedback from the environment, robot learning is undergoing a search for quantification and evaluation mechanisms, as well as for methods for scaling up the complexity of learning tasks. This special issue of Autonomous Robots will focus on novel robot learning applications and quantification of learning in autonomous robots. We are soliciting papers describing finished work preferably involving real manipulator or mobile robots. We invite submissions from all areas in AI and Machine Learning, Mobile Robotics, Machine Vision, Dexterous Manipulation, and Artificial Life that address robot learning. Submitted papers should be delivered by June 1, 1997. Authors intending to submit a manuscript should contact Henry Hexmoor as soon as possible to discuss paper ideas and suitability for this issue. Manuscripts should be typed or laser-printed in English (with American spelling preferred) and double-spaced. Both paper and electronic submission are possible, as described below. For paper submissions, send five (5) copies of submitted papers (hard-copy only) to: Dr. Henry Hexmoor Department of Computer Science State University of New York at Buffalo 226 Bell Hall Buffalo, NY 14260-2000 U.S.A. PHONE: 716-645-3197 FAX: 716-645-3464 For electronic submissions, use Postscript format, ftp the file to ftp.cs.buffalo.edu, and send an email notification to hexmoor@cs.buffalo.edu Detailed ftp instructions: compress your-paper (both Unix compress and gzip commands are ok) ftp ftp.cs.buffalo.edu (but check in case it has changed) give anonymous as your login name give your e-mail address as password set transmission to binary (just type the command BINARY) cd to users/hexmoor/ put your-paper send me an email notification hexmoor@cs.buffalo.edu to let me know you transferred the paper Editoral Board: James Albus, NIST, USA Peter Bonasso, NASA Johnson Space Center, USA Enric Celaya, Institut de Robotica i Informatica Industrial, Spain Adam J. Cheyer, SRI International, USA Keith L. Doty, University of Florida, USA Marco Dorigo, Universite' Libre de Bruxelles, Belgium Judy Franklin, Mount Holyoke College, USA Rod Grupen, University of Mass, USA John Hallam, University of Edinburgh, UK Inman Harvey, COGS, Univ. of Sussex, UK Gillian Hayes, University of Edinburgh, UK James Hendler, University of Maryland, USA David Hinkle, Johns Hopkins University, USA R James Firby, University of Chicago, USA Ian Horswill, Northwestern University, USA Sven Koenig, Carnegie Mellon University, USA Kurt Konolige, SRI International, USA David Kortenkamp, NASA Johnson Space Center, USA Francois Michaud, Brandeis University, USA Robin R. Murphy, Colorado School of Mines, USA Jose del R. MILLAN, Joint Research Centre of the EU, Italy Amitabha Mukerjee, IIT, India David J. Musliner, Honeywell Technology Center, USA Ulrich Nehmzow, University of Manchester, UK Tim Smithers, Universidad del Pai's Vasco, Spain Martin Nilsson, Swedish Institute of Computer Science, Sweden Stefano Nolfi, Institute of Psychology, C.N.R., Italy Tony J Prescott, University of Sheffield, UK Ashwin Ram, Georgia Institute of Technology, USA Alan C. Schultz, Naval Research Laboratory, USA Noel Sharkey, Sheffield University, UK Chris Thornton, UK Francisco J. Vico, Campus Universitario de Teatinos, Spain Brian Yamauchi, Naval Research Laboratory, USA Uwe R. Zimmer, Schloss Birlinghoven, Germany Relevant Dates: August 15, 1997 submission deadline November 15, 1997 review deadline December 1, 1997 acceptance/rejection notifications to the authors