A Bayesian approach to on-line learning

Manfred Opper, Ole Winther

    Research output: Chapter in Book/Published conference outputChapter


    Online learning is discussed from the viewpoint of Bayesian statistical inference. By replacing the true posterior distribution with a simpler parametric distribution, one can define an online algorithm by a repetition of two steps: An update of the approximate posterior, when a new example arrives, and an optimal projection into the parametric family. Choosing this family to be Gaussian, we show that the algorithm achieves asymptotic efficiency. An application to learning in single layer neural networks is given.
    Original languageEnglish
    Title of host publicationOn-line learning in neural networks
    EditorsDavid Saad
    Place of PublicationCambridge
    PublisherCambridge University Press
    Number of pages16
    ISBN (Print)0262194163
    Publication statusPublished - Jan 1999

    Publication series

    NamePublications of the Newton Institute
    PublisherCambridge University Press

    Bibliographical note

    Copyright of Cambridge University Press Available on Google Books


    • Online learning
    • Bayesian statistical inference
    • asymptotic efficiency
    • neural networks


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