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Neural Networks for Invariant Pattern Recognition

Record type: Conference or Workshop Item (Other)

In this paper, we discuss a methodology for applying feedforward networks to problems of invariant pattern recognition. We present the Group Representation Network (GRN), a type of feedforward network with the property that its output is invariant under a group of transformations of its input. Since the invariance of such a network is inbuilt, it does not need to be learned. Consequently it is capable of a better generalization performance than a conventional network for solving the same symmetric problem. In addition, the GRN has fewer free parameters than connections and we can hence expect it to train faster than an ordinary network of the same connectivity.

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Citation

Wood, J. and Shawe-Taylor, J. (1995) Neural Networks for Invariant Pattern Recognition At Proc. of European Symposium on Artificial Neural Networks. , pp. 253-8.

More information

Published date: 1995
Additional Information: Organisation: D facto Address: Brussels
Venue - Dates: Proc. of European Symposium on Artificial Neural Networks, 1995-01-01
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 250473
URI: http://eprints.soton.ac.uk/id/eprint/250473
PURE UUID: b2c15f84-ab5f-4f50-838d-3126dc817865

Catalogue record

Date deposited: 01 Jun 1999
Last modified: 18 Jul 2017 10:41

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Contributors

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

University divisions


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