Empirical Inference
We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, which traditionally requires solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach.
| Author(s): | Collobert, R. and Sinz, F. and Weston, J. and Bottou, L. |
| Links: | |
| Journal: | Journal of Machine Learning Research |
| Volume: | 7 |
| Pages: | 1687-1712 |
| Year: | 2006 |
| Month: | August |
| Day: | 0 |
| BibTeX Type: | Article (article) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@article{3765,
title = {Large Scale Transductive SVMs},
journal = {Journal of Machine Learning Research},
abstract = {We show how the Concave-Convex Procedure can be applied
to the optimization of Transductive SVMs, which traditionally requires solving
a combinatorial search problem. This
provides for the first time a highly scalable algorithm in the nonlinear case.
Detailed experiments verify the utility of our approach.},
volume = {7},
pages = {1687-1712},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = aug,
year = {2006},
author = {Collobert, R. and Sinz, F. and Weston, J. and Bottou, L.},
month_numeric = {8}
}