Chapter

Advanced Lectures on Machine Learning

Volume 2600 of the series Lecture Notes in Computer Science pp 65-117

Date:

Bayesian Kernel Methods

  • Alexander J. SmolaAffiliated withRSISE, The Australian National University
  • , Bernhard SchölkopfAffiliated withMax Planck Institut für Biologische Kybernetik

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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.