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Learning with Local and Global Consistency

2003

Technical Report

ei


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.

Author(s): Zhou, D. and Bousquet, O. and Lal, TN. and Weston, J. and Schölkopf, B.
Number (issue): 112
Year: 2003
Month: June
Day: 0

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics, Tuebingen, Germany

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex

@techreport{2293,
  title = {Learning with Local and Global Consistency},
  author = {Zhou, D. and Bousquet, O. and Lal, TN. and Weston, J. and Sch{\"o}lkopf, B.},
  number = {112},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, Tuebingen, Germany},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2003},
  month_numeric = {6}
}