Learning with Local and Global Consistency

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