Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature spaces.
1998
Conference Paper
ei
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}, doi = {} } |