IS = { zkontrolovano 31 Jan 2011 },
  UPDATE  = { 2010-07-20 },
  author =     {Sonnenburg, S{\" o}ren and Franc, Vojt{\v e}ch},
  title =      {COFFIN: A Computational Framework for Linear SVMs},
  booktitle =  {Proceedings of the 27th Annual International 
                Conference on Machine Learning (ICML 2010)},
  isbn =       {978-1-60558-907-7},
  venue =      {Haifa, Israel},
  year =       {2010},
  month =      {June},
  day =        {21--24},
  psurl =      {[Sonnenburg-COFFIN-ICML10.pdf]},
  publisher =  {Omnipress},
  authorship = {50-50},
  address =    {Madison, USA},
  keywords =   {support vector machines, large scale learning},
  annote = { In a variety of applications, kernel machines such as
    Support Vector Machines (SVMs) have been used with great success
    often delivering stat e-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, i.e., 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. Source code, data sets
    and scripts are freely available from
    http://sonnenburgs.de/soeren/coffin. },
  project =    {PERG04-GA-2008-239455 SEMISOL, FP7-ICT-247525 HUMAVIPS},
  pages   =    {999--1006},
  book_pages = {1262},