@inproceedings{5610,
  title = {Joint Kernel Support Estimation for Structured Prediction},
  journal = {Proceedings of the NIPS 2008 Workshop on "Structured Input - Structured Output" (NIPS SISO 2008)},
  abstract = {We present a new technique for structured
  prediction that works in a hybrid generative/
  discriminative way, using a one-class
  support vector machine to model the joint
  probability of (input, output)-pairs in a joint
  reproducing kernel Hilbert space.
  Compared to discriminative techniques, like
  conditional random elds or structured out-
  put SVMs, the proposed method has the advantage
  that its training time depends only
  on the number of training examples, not on
  the size of the label space. Due to its generative
  aspect, it is also very tolerant against
  ambiguous, incomplete or incorrect labels.
  Experiments on realistic data show that our
  method works eciently and robustly in situations
  for which discriminative techniques
  have computational or statistical problems.},
  pages = {1-4},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2008},
  author = {Lampert, CH. and Blaschko, M.},
  month_numeric = {12}
}