Empirical Inference
Empirical Inference
Empirical Inference
We address in this paper the question of how the knowledge of the marginal distribution $P(x)$ can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into account this distribution for regularization and provide links to existing graph-based semi-supervised learning algorithms. We also propose practical implementations.
| Author(s): | Bousquet, O. and Chapelle, O. and Hein, M. |
| Links: | |
| Book Title: | Advances in Neural Information Processing Systems 16 |
| Journal: | Advances in Neural Information Processing Systems |
| Pages: | 1221-1228 |
| Year: | 2004 |
| Month: | June |
| Day: | 0 |
| Editors: | Thrun, S., L. Saul, B. Sch{\"o}lkopf |
| Publisher: | MIT Press |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Cambridge, MA, USA |
| Event Name: | Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 1 |
| Electronic Archiving: | grant_archive |
| ISBN: | 0-262-20152-6 |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{2260,
title = {Measure Based Regularization},
journal = {Advances in Neural Information Processing Systems},
booktitle = {Advances in Neural Information Processing Systems 16},
abstract = {We address in this paper the question of how the knowledge of the marginal distribution $P(x)$ can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into account this distribution for regularization and provide links to existing graph-based semi-supervised learning algorithms. We also propose practical implementations.},
pages = {1221-1228},
editors = {Thrun, S., L. Saul, B. Sch{\"o}lkopf},
publisher = {MIT Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Cambridge, MA, USA},
month = jun,
year = {2004},
author = {Bousquet, O. and Chapelle, O. and Hein, M.},
month_numeric = {6}
}