From regularization operators to support vector kernels

Smola, AJ; Schölkopf, B

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From regularization operators to support vector kernels

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Schölkopf, B
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Smola, A., & Schölkopf, B. (1998). From regularization operators to support vector kernels. In M. Jordan, M. Kearns, & S. Solla (Eds.), Advances in Neural Information Processing Systems 10 (pp. 343-349). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E86D-F

Abstract

We derive the correspondence between regularization operators used in Regularization Networks and Hilbert Schmidt Kernels appearing in Sup-port Vector Machines. More specifica1ly, we prove that the Green's Func-tions associated with regularization operators are suitable Support Vector Kernels with equivalent regularization properties. As a by-product we show that a large number of Radial Basis Functions namely condition-ally positive definite functions may be used as Support Vector kernels.