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4th Neural Computation and Psychology Workshop, London, 9–11 by Mike Page (auth.), John A. Bullinaria BSc, MSc, PhD, David

By Mike Page (auth.), John A. Bullinaria BSc, MSc, PhD, David W. Glasspool BSc, Msc, George Houghton BA, MSc, PhD (eds.)

This quantity collects jointly refereed models of twenty-five papers provided on the 4th Neural Computation and Psychology Workshop, held at collage collage London in April 1997. The "NCPW" workshop sequence is now good tested as a full of life discussion board which brings jointly researchers from such various disciplines as man made intelligence, arithmetic, cognitive technological know-how, desktop technological know-how, neurobiology, philosophy and psychology to debate their paintings on connectionist modelling in psychology. the overall topic of this fourth workshop within the sequence used to be "Connectionist Repre­ sentations", an issue which not just attracted individuals from most of these fields, yet from allover the realm to boot. From the perspective of the convention organisers targeting representational concerns had the virtue that it instantly concerned researchers from all branches of neural computation. Being so relevant either to psychology and to connectionist modelling, it truly is one region approximately which all people within the box has their very own robust perspectives, and the variety and caliber of the displays and, simply as importantly, the dialogue which them, definitely attested to this.

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8] Holland, J. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press. A Generative Learning Algorithm that uses Structural Knowledge of the Input Domain yields a better Multi-layer Percept ron Elena Perez-Miiiana Department of Artificial Intelligence, University of Edinburgh Edinburgh, Scotland Abstract Many classifier applications have been developed using the Multi-layer perceptron (MLP) model as representation form. The main difficulty found in designing an architecture based on the model has been, for the most part, induced by a lack of understanding of what each of an MLP's network components embodies.

ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review 1992, 99: 22-44 5. Poggio T, Edelman S. A network that learns to recognize three dimensional objects. Nature (London) 1990,343: 263-266 6. Bishop CM. Neural networks for pattern recognition. Clarendon Press, Oxford, 1995 7. Moody JE, Darken Cl Fast learning in networks oflocally tuned processing units. Neural Computation 1989,1: 281-294 8. Poggio T, Girosi F. Networks for approximation and learning. Proceedings of the IEEE 1990,78: 1481-1497 9.

The main features of this particular model are: (a) The input set is comprised of a number of N-dimension vectors XP E R n , P) (X P)t -- (x Pl' x P2' •••'xn ' (1) where p indexes the occurrence of X in a training set; (b) the mathematical definition of the one-hidden layer network that has to be created for recognising all its members has the following form: I Ci = O'(~)Wik hk)) (2) j=o where n hk = O'(L: (Wkj x~)) (3) j=O corresponds to the output of a generic hidden unit hk of the architecture; represents the activation function employed, which subsequently will be considered to be the sigmoid unless otherwise stated; 0' 49 (c) the network produces as output a class index C = (Cl,··,Cj,··,cm ), Ci E {O, I} and C E (.

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