@article{FraSon09a,
  author =   {Vojtech Franc and S\"oren Sonnenburg},
  title =    {Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization},
  journal =  {Journal of Machine Learning Research},
  year =     {2009},
  pages = {2157--2192},
  month = {October},
  pdf = {http://www.jmlr.org/papers/volume10/franc09a/franc09a.pdf},
  volume = {10},
  editor =   {M.~Sebag},
  abstract = {We have developed an optimized cutting plane algorithm (OCA) for solving large scale risk
  minimization problems. We prove that the number of iterations OCA requires to converge
  to a epsilon-precise solution is approximately linear in the sample size. We derive OCAS, an OCA
  based linear binary Support Vector Machine (SVM) solver, and OCAM, a linear multi-class
  SVM solver. In an extensive empirical evaluation we show that OCAS outperforms current
  state of the art SVM solvers, like SVMlight, SVMperf and BMRM, achieving speedups of
  over 1,200 on some datasets over SVMlight and 29 over SVMperf, while obtaining the same
  precise Support Vector solution. OCAS even in the early optimization steps shows often
  faster convergence than the so far in this domain prevailing approximative methods SGD
  and Pegasos. In addition, our proposed linear multi-class SVM solver OCAM achieves
  speedups of up to 10 compared to SVMmulti-class . Finally, we apply OCAS and OCAM
  to two real-world applications, the problem of human acceptor splice site detection and
  malware detection. Effectively parallelizing OCAS we achieve state-of-the art results on
  the acceptor splice site recognition problem only by being able to learn on all the available
  50 million examples in a 12 million dimensional feature space.}
}