Measure Based Regularization
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.
Cite
Text
Bousquet et al. "Measure Based Regularization." Neural Information Processing Systems, 2003.Markdown
[Bousquet et al. "Measure Based Regularization." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/bousquet2003neurips-measure/)BibTeX
@inproceedings{bousquet2003neurips-measure,
title = {{Measure Based Regularization}},
author = {Bousquet, Olivier and Chapelle, Olivier and Hein, Matthias},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {1221-1228},
url = {https://mlanthology.org/neurips/2003/bousquet2003neurips-measure/}
}