@inbook{4435,
  title = {Trading Convexity for Scalability},
  booktitle = {Large Scale Kernel Machines},
  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 nonconvexity
  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 = {275-300},
  series = {Neural Information Processing},
  editors = {Bottou, L. , O. Chapelle, D. DeCoste, J. Weston},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2007},
  slug = {4435},
  author = {Collobert, R. and Sinz, F. and Weston, J. and Bottou, L.},
  month_numeric = {9}
}