Measure Based Regularization
Published 9 December 2003
Olivier Bousquet, Olivier Chapelle, Matthias Hein
Citations118
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TL;DR
This paper proposes three theoretical methods for taking into account this distribution P(x) for regularization and provides links to existing graph-based semi-supervised learning algorithms.
Abstract
We address in this paper the question of how the knowledge of the\nmarginal distribution $P(x)$ can be incorporated in a learning\nalgorithm. We suggest three theoretical methods for taking into\naccount this distribution for regularization and provide links to\nexisting graph-based semi-supervised learning algorithms. We also\npropose practical implementations.
Keywords
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