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0899-7667
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1530-888X
2014 Impact factor:
2.21

Neural Computation

May 1995, Vol. 7, No. 3, Pages 580-593
(doi: 10.1162/neco.1995.7.3.580)
© 1995 Massachusetts Institute of Technology
Bayesian Self-Organization Driven by Prior Probability Distributions
Article PDF (790.22 KB)
Abstract

Recent work by Becker and Hinton (1992) shows a promising mechanism, based on maximizing mutual information assuming spatial coherence, by which a system can self-organize to learn visual abilities such as binocular stereo. We introduce a more general criterion, based on Bayesian probability theory, and thereby demonstrate a connection to Bayesian theories of visual perception and to other organization principles for early vision (Atick and Redlich 1990). Methods for implementation using variants of stochastic learning are described.