@inproceedings{3931,
  title = {A Continuation Method for Semi-Supervised SVMs},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  abstract = {Semi-Supervised Support Vector Machines
  (S3VMs) are an appealing method for using
  unlabeled data in classification: their objective
  function favors decision boundaries
  which do not cut clusters. However their
  main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances.
  In this paper we propose to use a
  global optimization technique known as continuation
  to alleviate this problem. Compared
  to other algorithms minimizing the
  same objective function, our continuation
  method often leads to lower test errors.},
  pages = {185-192},
  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 = {3931},
  author = {Chapelle, O. and Chi, M. and Zien, A.},
  month_numeric = {6}
}