IS = { zkontrolovano 26 Feb 2010 },
  UPDATE  = { 2010-02-25 },
 author =        {Sonnenburg, Soeren and Franc, Vojtech},
 title =         {{COFFIN:} A Computational Framework for Linear {SVMs}},
 institution =   {Center for Machine Perception, K13133 FEE Czech Technical
 address =       {Prague, Czech Republic},
 year =  {2009},
 month =         {December},
 type =  {Research Report},
 number =        {CTU--CMP--2009--23},
 issn =  {1213-2365},
 pages =         {14},
 figures =       {2},
 authorship =    {50-50},
 psurl =         {[Sonnenburg-TR-2009-23.pdf]},
 project =       {PERG04-GA-2008-239455},
 annote = {In a variety of applications, kernel machines such as
   Support Vector Machines (SVMs) have been used with great success
   often delivering state-of-the-art results. Using the kernel trick,
   they work on several domains and even enable heterogeneous data
   fusion by concatenating feature spaces or multiple kernel
   learning. Unfortunately, they are not suited for truly large-scale
   applications since they suffer from the curse of supporting
   vectors, e.g., the speed of applying SVMs decays linearly with the
   number of support vectors. In this paper we develop COFFIN --- a
   new training strategy for linear SVMs that effectively allows the
   use of on demand computed kernel feature spaces and virtual
   examples in the primal. With linear training and prediction effort
   this framework leverages SVM applications to truly large-scale
   problems: As an example, we train SVMs for human splice site
   recognition involving 50 million examples and sophisticated string
   kernels. Additionally, we learn an SVM based gender detector on 5
   million examples on low-tech hardware and achieve beyond the
   state-of-the-art accuracies on both tasks.},
 keywords =      {support vector machines, large-scale learning,