Statistical mechanics of mutual information maximization

R. Urbanczik*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish
    Pages (from-to)685-691
    Number of pages7
    JournalEurophysics Letters
    Issue number5
    Publication statusPublished - Mar 2000

    Bibliographical note

    Copyright of EDP Sciences


    • unsupervised learning procedure
    • networks
    • supervised learning


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