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

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.

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 Creators:
Schölkopf, B1, Author           
Knirsch, P, Author           
Smola, AJ, Author           
Burges, C, Author
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1External Organizations, ou_persistent22              

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 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.

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 Dates: 1998-10
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 803
DOI: 10.1007/978-3-642-72282-0_12
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Title: 20th DAGM-Symposium
Place of Event: Stuttgart, Germany
Start-/End Date: 1998-09-29 - 1998-10-01

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Title: Mustererkennung 1998: 20. DAGM-Symposium Stuttgart, 29. September – 1. Oktober 1998
Source Genre: Proceedings
 Creator(s):
Levi, R, Editor
Schanz, M, Editor
Ahlers, R-J, Editor
May, F, Editor
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-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 125 - 132 Identifier: ISBN: 3-540-64935-2

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Title: Informatik Aktuell
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