Published September 1, 1989
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Journal Article
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Learning noisy patterns in a Hopfield network
- Creators
- Fontanari, J. F.
- Meir, R.
Abstract
We study the ability of a Hopfield network with a Hebbian learning rule to extract meaningful information from a noisy environment. We find that the network is able to learn an infinite number of ancestor patterns, having been exposed only to a finite number of noisy versions of each. We have also found that there is a regime where the network recognizes the ancestor patterns very well, while performing very poorly on the noisy patterns to which it had been exposed during the learning stage.
Additional Information
©1989 The American Physical Society Received 5 June 1989 The research at the California Institute of Technology was supported by Contract No. N00014-87-K-0377 from the U.S. Office of Naval Research. J.F.F. was partly supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico and R.M. is supported by the Weizmann Foundation.Files
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Additional details
- Eprint ID
- 6596
- Resolver ID
- CaltechAUTHORS:FONpra89
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2006-12-14Created from EPrint's datestamp field
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