@InProceedings{Franc-SVMbyML-ICML11,
  IS = { zkontrolovano 11 Jan 2012 },
  UPDATE  = { 2011-10-03 },
  author =     {Franc, Vojt{\v e}ch and Zien, Alex and Bernhard, Sch{\" o}lkopf},
  title =      {Support Vector Machines as Probabilistic Models},
  booktitle =  {Proceedings of the 28th Annual International
                Conference on Machine Learning (ICML 2011)},
  editor =     {Getoor, Lise and Scheffer, Tobias},
  isbn =       {978-1-4503-0619-5},
  venue =      {Bellevue, USA},
  year =       {2011},
  month =      {June,Jully},
  day =        {28--2},
  psurl =      {ftp://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-SVMbyML-ICML11.pdf},
  publisher =  {ACM},
  address =    {New York, USA},
  authorship = {34-33-33},
  keywords =   {support vector machines, maximum likelihood},
  annote = { We show how the SVM can be viewed as a maximum likelihood
    estimate of a class of probabilistic models. This model class can
    be viewed as a reparametrization of the SVM in a similar vein to
    the v-SVM reparametrizing the classical (C-)SVM. It is not
    discriminative, but has a non-uniform marginal. We illustrate the
    benefits of this new view by re-deriving and re-investigating two
    established SVM-related algorithms.},
  project =    {1M0567, PERG04-GA-2008-239455 SEMISOL,
                FP7-ICT-247525 HUMAVIPS only EU},
  pages   =    {665--672},
  book_pages = {1217},
}