Bayesian kernel methods

Alexander J. Smola*, Bernhard Schölkopf

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    19 Citations (Scopus)

    Abstract

    Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    EditorsShahar Mendelson, Alexander J. Smola
    PublisherSpringer Verlag
    Pages65-117
    Number of pages53
    ISBN (Print)9783540005292
    DOIs
    Publication statusPublished - 2003

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2600
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

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