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.

Download

Full text not available from this repository.

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 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.

Item Type: Article
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science
ePrint ID: 250474
Date Deposited: 01 Jun 1999
Last Modified: 27 Mar 2014 19:51
Further Information:Google Scholar
ISI Citation Count:3
URI: http://eprints.soton.ac.uk/id/eprint/250474

Actions (login required)

View Item View Item