Joint Kernel Support Estimation for Structured Prediction

2008

Conference Paper

ei

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.

@inproceedings{5610,
  title = {Joint Kernel Support Estimation for Structured Prediction},
  author = {Lampert, CH. and Blaschko, M.},
  journal = {Proceedings of the NIPS 2008 Workshop on "Structured Input - Structured Output" (NIPS SISO 2008)},
  pages = {1-4},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2008},
  doi = {},
  month_numeric = {12}
}