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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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 Urheber:
Zhou, D1, 2, Autor           
Bousquet, O1, 2, Autor           
Lal, TN1, 2, Autor           
Weston, J1, 2, Autor           
Schölkopf, B1, 2, Autor           
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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 Zusammenfassung: 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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 Datum: 2003-06
 Publikationsstatus: Erschienen
 Seiten: 8
 Ort, Verlag, Ausgabe: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: Reportnr.: 112
BibTex Citekey: 2293
 Art des Abschluß: -

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Titel: Technical Report of the Max Planck Institute for Biological Cybernetics
Genre der Quelle: Reihe
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Seiten: - Band / Heft: 112 Artikelnummer: - Start- / Endseite: - Identifikator: -