Bayesian Kernel Methods

Smola, AJ; Schölkopf, B

doi:10.1007/3-540-36434-X_3

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Bayesian Kernel Methods

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Smola, A., & Schölkopf, B. (2003). Bayesian Kernel Methods. In S. Mendelson, & A. Smola (Eds.), Advanced Lectures on Machine Learning: Machine Learning Summer School 2002 Canberra, Australia, February 11–22, 2002 (pp. 65-117). Berlin, Germany: Springer. doi:10.1007/3-540-36434-X_3.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DD96-0

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.