Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  Learning with Local and Global Consistency

Zhou, D., Bousquet, O., Lal, T., Weston, J., & Schölkopf, B.(2003). Learning with Local and Global Consistency (112).

Item is

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Zhou, D1, Autor           
Bousquet, O1, Autor           
Lal, TN1, Autor           
Weston, J1, Autor           
Schölkopf, B1, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 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.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2003-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: Reportnr.: 112
BibTex Citekey: 2293
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle

einblenden: