Revisiting Boltzmann learning: parameter estimation in Markov random fields

Lars Kai Hansen, Lars Nonboe Andersen, Ulrik Kjems, Jan Larsen

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    Abstract

    This article presents a generalization of the Boltzmann machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization and generalization in the context of Boltzmann machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov field. The regularized adaptation produces a parameter set that closely resembles the “teacher” parameters, hence, will produce segmentations that closely reproduce those of the inhomogeneous teacher network
    Original languageEnglish
    Title of host publicationProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
    VolumeVolume 6
    PublisherIEEE
    Publication date1996
    Pages3394-3397
    ISBN (Print)07-80-33192-3
    DOIs
    Publication statusPublished - 1996
    Event1996 IEEE International Conference on Acoustics, Speech and Signal Processing - Atlanta, GA, United States
    Duration: 7 May 199610 May 1996
    http://www.eng.auburn.edu/~sjreeves/ICASSP/

    Conference

    Conference1996 IEEE International Conference on Acoustics, Speech and Signal Processing
    CountryUnited States
    CityAtlanta, GA
    Period07/05/199610/05/1996
    Internet address

    Bibliographical note

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