@inproceedings{FraSon08,
  author =   {Vojtech Franc and S\"oren Sonnenburg},
  title =    {{OCAS} Optimized cutting plane algorithm for support vector machines},
  booktitle = {Proceedings of the 25nd International Machine Learning Conference},
  publisher = {ACM Press},
  year =     {2008},
  month = {June},
  pdf =  {http://sonnenburgs.de/soeren/publications/FraSon08.pdf},
  url = {http://cmp.felk.cvut.cz/~xfrancv/ocas/html/index.html},
  dataset = {http://mloss.org/software/view/85/},
  abstract = {We have developed a new Linear Support Vector Machine (SVM) training algorithm called
		  OCAS. Its computational effort scales linearly with the sample size.
		  In an extensive empirical evaluation OCAS significantly outperforms current state of the art SVM
		  solvers, like SVMLight, SVMPerf and BMRM, achieving speedups of over
		  1,000 on some datasets over SVMLight and 20 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. Effectively parallelizing 
		  OCAS we were able to train on a dataset of size 15 million examples
		  (itself about 32GB in size) in just 671 seconds --- a competing string kernel SVM
		  required 97,484 seconds to train on 10 million examples sub-sampled from this
		  dataset.}
}