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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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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84020

Knirsch,  P
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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. Mustererkennung 1998 --- 20. DAGM-Symposium, 125-132.


Cite as: http://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.