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Using a feed-forward network to incorporate the relation between attractees and attractors in a generalized discrete Hopfield network
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UNSPECIFIED (1996) Using a feed-forward network to incorporate the relation between attractees and attractors in a generalized discrete Hopfield network. INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 7 (3). pp. 273-286. ISSN 0129-0657
Full text not available from this repository.Abstract
This paper demonstrates how a feedforward network with constant connection matrices may be used to train a Hopfield style network for pattern recognition. The connection matrix of the Hopfield style network is asymmetric and its diagonal is non-zero. The Hopfield style network referred to as a GDHN is trained to incorporate a relation between attractees and attractors. The attractees represent class samples and the attractors represent class prototypes. The feedforward network is trained using a gradient descent method. Gradients are fed forward in the network to obtain a gradient for a cost function.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
| Journal or Publication Title: | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
| Publisher: | WORLD SCIENTIFIC PUBL CO PTE LTD |
| ISSN: | 0129-0657 |
| Date: | July 1996 |
| Volume: | 7 |
| Number: | 3 |
| Number of Pages: | 14 |
| Page Range: | pp. 273-286 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/18349 |
Data sourced from Thomson Reuters' Web of Knowledge
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