@inproceedings{2899,
  title = {Semi-Supervised Classification by Low Density Separation},
  journal = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005)},
  booktitle = {AISTATS 2005},
  abstract = {We believe that the cluster assumption is key
  to successful semi-supervised learning.
  Based on this, we propose three semi-supervised algorithms:
  1. deriving graph-based distances that emphazise low density regions
  between clusters, followed by training a standard SVM;
  2. optimizing the Transductive SVM objective function,
  which places the decision boundary in low density regions,
  by gradient descent;
  3. combining the first two to make
  maximum use of the cluster assumption.
  We compare with state of the art algorithms and demonstrate superior accuracy for the latter two methods.},
  pages = {57-64},
  editors = {Cowell, R. , Z. Ghahramani},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jan,
  year = {2005},
  slug = {2899},
  author = {Chapelle, O. and Zien, A.},
  month_numeric = {1}
}