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Measure Based Regularization
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.
@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}, slug = {2260}, author = {Bousquet, O. and Chapelle, O. and Hein, M.}, month_numeric = {6} }