Bayesian Kernel Methods
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Smola, Alexander
Schoelkopf, Bernhard
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Springer
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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Advanced Lectures on Machine Learning
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2037-12-31
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