Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Olivier Bousquet, Olivier Chapelle, Matthias Hein
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.