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.


Full text not available from this repository.


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
ISSNs: 0166-218X (print)
Organisations: Electronics & Computer Science
ePrint ID: 250474
Date :
Date Event
August 1996Published
Date Deposited: 01 Jun 1999
Last Modified: 18 Apr 2017 00:20
Further Information:Google Scholar

Actions (login required)

View Item View Item