Fast approximation of support vector kernel expansions, and an interpretation 
of clustering as approximation in feature spaces.

Schölkopf, B; Knirsch, P; Smola, AJ; Burges, C

doi:10.1007/978-3-642-72282-0_12

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Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature spaces.

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Schölkopf, B., Knirsch, P., Smola, A., & Burges, C. (1998). Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature spaces. In R. Levi, M. Schanz, R.-J. Ahlers, & F. May (Eds.), Mustererkennung 1998: 20. DAGM-Symposium Stuttgart, 29. September – 1. Oktober 1998 (pp. 125-132). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E950-5

Abstract

Kernel-based learning methods provide their solutions as expansions in terms of a kernel. We consider the problem of reducing the computational complexity of evaluating these expansions by approximating them using fewer terms. As a by-product, we point out a connection between clustering and approximation in reproducing kernel Hilbert spaces generated by a particular class of kernels.