@inproceedings{3917,
  title = {Trading Convexity for Scalability},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  abstract = {Convex learning algorithms, such as Support Vector Machines (SVMs), are
  often seen as highly desirable because they offer strong practical
  properties and are amenable to theoretical analysis.  However, in this work
  we show how non-convexity can provide scalability advantages over
  convexity.  We show how concave-convex programming can be applied to produce
  (i) faster SVMs where training errors are no longer support vectors, and
  (ii) much faster Transductive SVMs.},
  pages = {201-208},
  editors = {Cohen, W. W., A. Moore},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  institution = {Association for Computing Machinery},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2006},
  slug = {3917},
  author = {Collobert, R. and Sinz, F. and Weston, J. and Bottou, L.},
  month_numeric = {6}
}