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article · Europhysics Letters (EPL) · 2000

Statistical mechanics of mutual information maximization

Robert Urbanczik

Cited by 2Open access

Abstract

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.

Cite this paper

APA 7

Urbanczik, R. (2000). Statistical mechanics of mutual information maximization. Europhysics Letters (EPL), 49(5), 685–691. https://doi.org/10.1209/epl/i2000-00205-7

MLA 9

Urbanczik, Robert. “Statistical mechanics of mutual information maximization.” Europhysics Letters (EPL), vol. 49, no. 5, 2000, pp. 685–91, https://doi.org/10.1209/epl/i2000-00205-7.

Chicago

Urbanczik, Robert. 2000. “Statistical mechanics of mutual information maximization.” Europhysics Letters (EPL) 49 (5): 685–91. https://doi.org/10.1209/epl/i2000-00205-7.

Harvard

Urbanczik, R. (2000) ‘Statistical mechanics of mutual information maximization’, Europhysics Letters (EPL), 49(5), pp. 685–691. Available at: https://doi.org/10.1209/epl/i2000-00205-7.

BibTeX
@article{urbanczik2000statistical,
  title = {{Statistical mechanics of mutual information maximization}},
  author = {Urbanczik, Robert},
  journal = {Europhysics Letters (EPL)},
  volume = {49},
  number = {5},
  pages = {685--691},
  year = {2000},
  doi = {10.1209/epl/i2000-00205-7}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

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