Incorporating Invariances in Non-Linear Support Vector Machines
Abstract
The choice of an SVM kernel corresponds to the choice of a rep(cid:173) resentation of the data in a feature space and, to improve per(cid:173) formance, 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 non(cid:173) linear kernels. We show on a digit recognition task that the pro(cid:173) posed approach is superior to the Virtual Support Vector method, which previously had been the method of choice.
Cite
Text
Chapelle and Schölkopf. "Incorporating Invariances in Non-Linear Support Vector Machines." Neural Information Processing Systems, 2001.Markdown
[Chapelle and Schölkopf. "Incorporating Invariances in Non-Linear Support Vector Machines." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/chapelle2001neurips-incorporating/)BibTeX
@inproceedings{chapelle2001neurips-incorporating,
title = {{Incorporating Invariances in Non-Linear Support Vector Machines}},
author = {Chapelle, Olivier and Schölkopf, Bernhard},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {609-616},
url = {https://mlanthology.org/neurips/2001/chapelle2001neurips-incorporating/}
}