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Bayesian Kernel Methods

Alexander J. Smola3 and Bernhard Schölkopf4

(3)  RSISE, The Australian National University, 0200, ACT Canberra, Australia
(4)  Max Planck Institut für Biologische Kybernetik, 72076 Tübingen, Germany
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.
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