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

1998

Conference Paper

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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.

Author(s): Schölkopf, B. and Knirsch, P. and Smola, AJ. and Burges, C.
Book Title: Mustererkennung 1998
Journal: Mustererkennung 1998 --- 20. DAGM-Symposium
Pages: 125-132
Year: 1998
Day: 0

Series: Informatik aktuell
Editors: P Levi and M Schanz and R-J Ahlers and F May
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 20th DAGM-Symposium
Event Place: Stuttgart, Germany

Address: Berlin, Germany
Digital: 0
ISBN: 3-540-64935-2
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@inproceedings{803,
  title = {Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature spaces.},
  author = {Sch{\"o}lkopf, B. and Knirsch, P. and Smola, AJ. and Burges, C.},
  journal = {Mustererkennung 1998 --- 20. DAGM-Symposium},
  booktitle = {Mustererkennung 1998},
  pages = {125-132},
  series = {Informatik aktuell},
  editors = {P Levi and M Schanz and R-J Ahlers and F May},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  year = {1998}
}