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Representation Theory and Invariant Neural Networks

Record type: Article

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 generalization abilities and may also learn faster than corresponding networks without in-built invariance. We hope in the future to generalize this theory to approximate invariance under continuous groups.

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Citation

Wood, J. and Shawe-Taylor, J. (1996) Representation Theory and Invariant Neural Networks Discrete Applied Mathematics, 69, (1-2), pp. 33-60.

More information

Published date: August 1996
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 259800
URI: http://eprints.soton.ac.uk/id/eprint/259800
ISSN: 0166-218X
PURE UUID: 9a081952-ce1f-43f9-bc77-a8f45409c17d

Catalogue record

Date deposited: 20 Aug 2004
Last modified: 18 Jul 2017 09:19

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Contributors

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

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