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), 33-60.

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Description/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 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.

Item Type: Article
ISSNs: 0166-218X
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science
ePrint ID: 259800
Date Deposited: 20 Aug 2004
Last Modified: 27 Mar 2014 20:02
Further Information:Google Scholar
ISI Citation Count:3
URI: http://eprints.soton.ac.uk/id/eprint/259800

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