Empirical Inference
Conference Paper
2002
Incorporating Invariances in Non-Linear Support Vector Machines
PDF Web
Empirical Inference
The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels. We show on a digit recognition task that the proposed approach is superior to the Virtual Support Vector method, which previously had been the method of choice.
| Author(s): | Chapelle, O. and Schölkopf, B. |
| Links: | |
| Book Title: | Advances in Neural Information Processing Systems 14 |
| Journal: | Advances in Neural Information Processing Systems |
| Pages: | 609-616 |
| Year: | 2002 |
| Month: | September |
| Day: | 0 |
| Editors: | TG Dietterich and S Becker and Z Ghahramani |
| Publisher: | MIT Press |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Cambridge, MA, USA |
| Event Name: | 15th Annual Neural Information Processing Systems Conference (NIPS 2001) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 0-262-04208-8 |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{1820,
title = {Incorporating Invariances in Non-Linear Support Vector Machines },
journal = {Advances in Neural Information Processing Systems},
booktitle = {Advances in Neural Information Processing Systems 14},
abstract = {The choice of an SVM kernel corresponds to the choice of a
representation of the data in a feature space and, to
improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels. We show on a
digit recognition task that the proposed approach is
superior to the Virtual Support Vector method, which previously had been the method of choice.},
pages = {609-616},
editors = {TG Dietterich and S Becker and Z Ghahramani},
publisher = {MIT Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Cambridge, MA, USA},
month = sep,
year = {2002},
author = {Chapelle, O. and Sch{\"o}lkopf, B.},
month_numeric = {9}
}