Kernel Methods in Machine Learning

Hofmann, T; Schölkopf, B; Smola, AJ

doi:10.1214/009053607000000677

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Kernel Methods in Machine Learning

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Hofmann, T
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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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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https://projecteuclid.org/download/pdfview_1/euclid.aos/1211819561
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Citation

Hofmann, T., Schölkopf, B., & Smola, A. (2008). Kernel Methods in Machine Learning. The Annals of Statistics, 36(3), 1171-1220. doi:10.1214/009053607000000677.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C8CF-6

Abstract

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data.