@inproceedings{4061,
  title = {Deterministic annealing for semi-supervised kernel machines},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  abstract = {An intuitive approach to utilizing unlabeled data in kernel-based
  classification algorithms is to simply treat the unknown labels as
  additional optimization variables. For margin-based loss functions,
  one can view this approach as attempting to learn low-density
  separators. However, this is a hard optimization problem to solve in
  typical semi-supervised settings where unlabeled data is abundant.
  The popular Transductive SVM algorithm is a
  label-switching-retraining procedure that is known to be susceptible
  to local minima.  In this paper, we present a global optimization
  framework for semi-supervised Kernel machines where an easier
  problem is parametrically deformed to the original hard problem and
  minimizers are smoothly tracked. Our approach is motivated from
  deterministic annealing techniques and involves a sequence of convex
  optimization problems that are exactly and efficiently solved. We
  present empirical results on several synthetic and real world
  datasets that demonstrate the effectiveness of our approach.},
  pages = {841-848},
  editors = {Cohen, W. W., A. Moore},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2006},
  slug = {4061},
  author = {Sindhwani, V. and Keerthi, SS. and Chapelle, O.},
  month_numeric = {6}
}