ML Anthology

Bayesian Self-Organization Driven by Prior Probability Distributions

Alan L. Yuille, Stelios M. Smirnakis, Lei Xu
NeCo 1995 pp. 580-595
doi:10.1162/NECO.1995.7.3.580 /neco/1995/yuille1995neco-bayesian/

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.

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Cite

Text

Yuille et al. "Bayesian Self-Organization Driven by Prior Probability Distributions." Neural Computation, 1995. doi:10.1162/NECO.1995.7.3.580

Markdown

[Yuille et al. "Bayesian Self-Organization Driven by Prior Probability Distributions." Neural Computation, 1995.](https://mlanthology.org/neco/1995/yuille1995neco-bayesian/) doi:10.1162/NECO.1995.7.3.580

BibTeX

@article{yuille1995neco-bayesian,
  title     = {{Bayesian Self-Organization Driven by Prior Probability Distributions}},
  author    = {Yuille, Alan L. and Smirnakis, Stelios M. and Xu, Lei},
  journal   = {Neural Computation},
  year      = {1995},
  pages     = {580-595},
  doi       = {10.1162/NECO.1995.7.3.580},
  volume    = {7},
  url       = {https://mlanthology.org/neco/1995/yuille1995neco-bayesian/}
}