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.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Olivier Bousquet, Olivier Chapelle, Matthias Hein
• Keywords: Graph-based Semi-supervised Learning, Learning Algorithms, Marginal Distribution, NIPS 2003, NIPS 2007