Boltzmann learning of parameters in cellular neural networks

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    Abstract

    The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified by unsupervised adaptation of an image segmentation cellular network. The learning rule is applied to adaptive segmentation of satellite imagery
    Original languageEnglish
    Title of host publicationProceedings of the 2nd Second International Workshop on Cellular Neural Networks and their Applications
    PublisherIEEE
    Publication date1992
    Pages62-67
    ISBN (Print)07-80-30875-1
    DOIs
    Publication statusPublished - 1992
    EventInternational Workshop on Cellular Neural Networks and their Applications - Munich, Germany
    Duration: 1 Jan 1992 → …
    Conference number: 2nd

    Conference

    ConferenceInternational Workshop on Cellular Neural Networks and their Applications
    Number2nd
    CityMunich, Germany
    Period01/01/1992 → …

    Bibliographical note

    Copyright: 1992 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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