Gaussian processes and SVM: Mean field results and leave-one-out

Manfred Opper, Ole Winther

    Research output: Chapter in Book/Published conference outputChapter


    In this chapter, we elaborate on the well-known relationship between Gaussian processes (GP) and Support Vector Machines (SVM). Secondly, we present approximate solutions for two computational problems arising in GP and SVM. The first one is the calculation of the posterior mean for GP classifiers using a `naive' mean field approach. The second one is a leave-one-out estimator for the generalization error of SVM based on a linear response method. Simulation results on a benchmark dataset show similar performances for the GP mean field algorithm and the SVM algorithm. The approximate leave-one-out estimator is found to be in very good agreement with the exact leave-one-out error.
    Original languageEnglish
    Title of host publicationAdvances in large margin classifiers
    EditorsAlex J. Smola, Peter Bartlett, Bernhard Schölkopf, Dale Schuurmans
    Place of PublicationCambridge, US
    Number of pages23
    ISBN (Print)0262194481
    Publication statusPublished - Oct 2000

    Bibliographical note

    Massachusetts Institute of Technology Press (MIT Press) Available on Google Books


    • Gaussian processes
    • support vector machines


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