A Short Introduction to Learning with Kernels

Schölkopf, B; Smola, AJ

doi:10.1007/3-540-36434-X_2

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A Short Introduction to Learning with Kernels

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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

Schölkopf, B., & Smola, A. (2003). A Short Introduction to Learning with Kernels. In S. Mendelson, & A. Smola (Eds.), Advanced Lectures on Machine Learning: Machine Learning Summer School 2002 Canberra, Australia, February 11–22, 2002 (pp. 41-64). Berlin, Germany: Springer. doi:10.1007/3-540-36434-X_2.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DDC4-8

Abstract

We briefly describe the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector optimization problem for classification and regression, the v-trick, various kernels and an overview over applications of kernel methods.