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  Measure Based Regularization

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D8F1-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-5375-A
Genre: Conference Paper

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 Creators:
Bousquet, O1, 2, Author              
Chapelle, O1, 2, Author              
Hein, M1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2004-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2260
 Degree: -

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Title: Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2003-12-09 - 2003-12-11

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Title: Advances in Neural Information Processing Systems 16
Source Genre: Proceedings
 Creator(s):
Thrun, S, Editor
Saul, LK, Editor
Schölkopf, B1, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1221 - 1228 Identifier: ISBN: 0-262-20152-6