Read e-book online 4th Neural Computation and Psychology Workshop, London, 9–11 PDF

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

ISBN-10: 1447115465

ISBN-13: 9781447115465

ISBN-10: 3540762086

ISBN-13: 9783540762089

This quantity collects jointly refereed types 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 verified as a full of life discussion board which brings jointly researchers from such different disciplines as synthetic intelligence, arithmetic, cognitive technological know-how, computing device 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 was once "Connectionist Repre­ sentations", a subject which not just attracted individuals from most of these fields, yet from allover the area to boot. From the viewpoint of the convention organisers concentrating on representational matters had the virtue that it instantly concerned researchers from all branches of neural computation. Being so crucial either to psychology and to connectionist modelling, it's one zone approximately which everybody within the box has their very own powerful perspectives, and the range and caliber of the displays and, simply as importantly, the dialogue which them, definitely attested to this.

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Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 788-793). Morgan Kaufmann. [7] Muggleton, S. ) (1992). Inductive Logic Programming. Academic Press. [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.

PM} in N-dimension space, called sites, corresponds to a partition of the space into 50 Figure 2: Voronoi Diagram, Delaunay Triangulation of the input domain convex regions, called Voronoi cells. Each cell represents a "region of influence" surrounding each site. In two dimensions the Voronoi cells are convex polygons whose edges agree with the perpendicular bisectors of the Delaunay triangle edges joining neighbouring sites. The triangulation results from connecting each pair of data sites that share a common Voronoi region boundary.

The weights from hidden layer to output layer are initially produced using the output of the hidden layer and the training data by applying a linear function. The vertex angle is chosen in such a way that the network would start as RBF. Then, the weights, centres and angle values are updated using error back propagation so that the network converges quickly. 6. cx=:-----1 i=1 (' i : input nodes j : hidden nodes k : output nodes p : number of patterns cos CJ ,=0 for ail k Figure 6: Block diagram of CSFN for training where api = Xpi if unit i is an input unit, Cij are the centres for the RBF network, Wij are the weights in an MLP, W is the half opening angle, which can be any value in the range [-11"/2,11"/2] and determines the different forms of the decision borders, i and j are the indices referring to units in the input and the hidden layer and p refers to the number of the patterns.

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4th Neural Computation and Psychology Workshop, London, 9–11 April 1997: Connectionist Representations by Mike Page (auth.), John A. Bullinaria BSc, MSc, PhD, David W. Glasspool BSc, Msc, George Houghton BA, MSc, PhD (eds.)


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