Measure Based Regularization

Bousquet, O; Chapelle, O; Hein, M

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Measure Based Regularization

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Bousquet, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Hein, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://papers.nips.cc/paper/2504-measure-based-regularization.pdf
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Zitation

Bousquet, O., Chapelle, O., & Hein, M. (2004). Measure Based Regularization. In S. Thrun, L. Saul, & B. Schölkopf (Eds.), Advances in Neural Information Processing Systems 16 (pp. 1221-1228). Cambridge, MA, USA: MIT Press.

Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-D8F1-4

Zusammenfassung

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.