@inproceedings{2333,
  title = {Learning with Local and Global Consistency},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 16},
  abstract = {We consider the general problem of learning from labeled and
  unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by 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.},
  pages = {321-328},
  editors = {S Thrun and LK Saul and B Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  slug = {2333},
  author = {Zhou, D. and Bousquet, O. and Lal, TN. and Weston, J. and Sch{\"o}lkopf, B.},
  month_numeric = {6}
}