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

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 Creators:
Schölkopf, B1, Author           
Knirsch, P2, Author           
Smola, AJ, Author
Burges, C, Author
Levi, Editor
P., Editor
Schanz, M., Editor
Ahlers, R.-J., Editor
May, F., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

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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
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 3-540-64935-2
URI: http://dl.acm.org/citation.cfm?id=755597CFID=86271708CFTOKEN=17835895
BibTex Citekey: 803
 Degree: -

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Title: 20th DAGM-Symposium
Place of Event: Stuttgart, Germany
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Title: Mustererkennung 1998 --- 20. DAGM-Symposium
Source Genre: Journal
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 125 - 132 Identifier: -