Statistical mechanics of mutual information maximization


An unsupervised learning procedure based on maximizing the mutual information between the outputs of two networks receiving different but statistically dependent inputs is analyzed (Becker S. and Hinton G., Nature, 355 (1992) 161). By exploiting a formal analogy to supervised learning in parity machines, the theory of zero-temperature Gibbs learning for the unsupervised procedure is presented for the case that the networks are perceptrons and for the case of fully connected committees.

Publication DOI:
Divisions: Aston University (General)
Additional Information: Copyright of EDP Sciences
Uncontrolled Keywords: unsupervised learning procedure,networks,supervised learning
Publication ISSN: 1286-4854
Last Modified: 02 Jan 2024 08:04
Date Deposited: 10 Aug 2009 11:01
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://iopscien ... ect=.iopscience (Publisher URL)
PURE Output Type: Article
Published Date: 2000-03
Authors: Urbanczik, R.



Version: Published Version

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