@techreport{2782,
  title = {Semi-Supervised Induction},
  abstract = {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.},
  number = {141},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, Tuebingen, Germany},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2004},
  author = {Yu, K. and Tresp, V. and Zhou, D.},
  month_numeric = {8}
}