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Learning algorithms and probability distributions in feed-forward and feed-back networks

Hopfield, J. J. (1987) Learning algorithms and probability distributions in feed-forward and feed-back networks. Proceedings of the National Academy of Sciences of the United States of America, 84 (23). pp. 8429-8433. ISSN 0027-8424. http://resolver.caltech.edu/CaltechAUTHORS:HOPpnas87

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Abstract

Learning algorithms have been used both on feed-forward deterministic networks and on feed-back statistical networks to capture input-output relations and do pattern classification. These learning algorithms are examined for a class of problems characterized by noisy or statistical data, in which the networks learn the relation between input data and probability distributions of answers. In simple but nontrivial networks the two learning rules are closely related. Under some circumstances the learning problem for the statistical networks can be solved without Monte Carlo procedures. The usual arbitrary learning goals of feed-forward networks can be given useful probabilistic meaning.


Item Type:Article
Additional Information:© 1987 by the National Academy of Sciences. Contributed by J. J. Hopfield, August 17, 1987. I acknowledge helpful conversations with D.W. Tank, E. Baum, and S. Solla. This work was supported by contract N00014-K-0377 from the Office of Naval Research. The publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.
Record Number:CaltechAUTHORS:HOPpnas87
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:HOPpnas87
Alternative URL:http://www.pnas.org/cgi/content/abstract/84/23/8429
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8460
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:14 Aug 2007
Last Modified:14 Nov 2014 19:20

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