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 and Applied Science > Electronics and Computer Science |
| Item ID: | 259800 |
| Date Deposited: | 20 Aug 2004 |
| Last Modified: | 09 Aug 2012 23:54 |
| Contributors: | Wood, J. (Author) Shawe-Taylor, J. (Author) |
| Date: | August 1996 |
| Status: | Published |
| Publisher: | Elsevier Science Publishers B. V. |
| Further Information: | Google Scholar |
| ISI Citation Count: | 3 |
| URI: | http://eprints.soton.ac.uk/id/eprint/259800 |
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