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Representation theory and invariant neural networks

Wood, J. and Shawe-Taylor, J. (1996) Representation theory and invariant neural networks Discrete Applied Mathematics, 69, (1-2), pp. 33-60.

Record type: Article

Abstract

A feedforward neural network is a computational device used for pattern recognition. In many recognition problems, certain transformations exist which, when applied to a pattern, leave its classification unchanged. Invariance under a given group of transformations is therefore typically a desirable property of pattern classifiers. In this paper, we present a methodology, based on representation theory, for the construction of a neural network invariant under any given finite linear group. Such networks show improved generalisation abilities and may also learn faster than corresponding networks without inbuilt invariance. We hope in the future to generalise this theory to approximate invariance under continuous groups.

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More information

Published date: August 1996
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 250474
URI: http://eprints.soton.ac.uk/id/eprint/250474
ISSN: 0166-218X
PURE UUID: 80f3abb8-efe1-4d19-b0ae-703ad4aebb91

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Date deposited: 01 Jun 1999
Last modified: 18 Jul 2017 10:41

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Contributors

Author: J. Wood
Author: J. Shawe-Taylor

University divisions

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