Learning with Local and Global Consistency

Zhou, D; Bousquet, O; Lal, TN; Weston, J; Schölkopf, B

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Learning with Local and Global Consistency

Zhou, D., Bousquet, O., Lal, T., Weston, J., & Schölkopf, B.(2003). Learning with Local and Global Consistency (112). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DC51-C Version Permalink: https://hdl.handle.net/21.11116/0000-0002-8C81-E

Genre: Report

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Creators:
Zhou, D^{1, 2}, Author
Bousquet, O^{1, 2}, Author
Lal, TN^{1, 2}, Author
Weston, J^{1, 2}, Author
Schölkopf, B^{1, 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 consider the learning problem in the transductive setting. Given a set of points of which only some are labeled, the goal is to
predict the label of the unlabeled points. A principled clue to
solve such a learning problem is the consistency assumption that a
classifying function should be sufficiently smooth with respect to
the structure revealed by these known labeled and unlabeled points. We present a simple
algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a
number of classification problems and demonstrates effective use of
unlabeled data.

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Dates: Date issued: 2003-06

Publication Status: Issued

Pages: 8

Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics

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Identifiers: Report Nr.: 112
BibTex Citekey: 2293

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Title: Technical Report of the Max Planck Institute for Biological Cybernetics

Source Genre: Series

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Pages: - Volume / Issue: 112 Sequence Number: - Start / End Page: - Identifier: -