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Semi-Supervised Induction


Technical Report


Considerable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.

Author(s): Yu, K. and Tresp, V. and Zhou, D.
Number (issue): 141
Year: 2004
Month: August
Day: 0

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

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

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

Links: PDF


  title = {Semi-Supervised Induction},
  author = {Yu, K. and Tresp, V. and Zhou, D.},
  number = {141},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, Tuebingen, Germany},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2004},
  month_numeric = {8}