IS = { zkontrolovano 15 Jan 2013 },
  UPDATE  = { 2012-12-27 },
  author =      {Antoniuk, Konstiantyn and Franc, Vojt{\v{e}}ch and Hlav{\'a}{\v c}, V{\'a}clav},
  title =       {Learning Markov Networks by Analytic Center Cutting Plane Method},
  year =        {2012},
  pages =       {2250-2253},
  booktitle =   {ICPR '12: Proceedings of 21st International Conference on Pattern Recognition},
  publisher =   {IEEE},
  address =     {New York, USA},
  isbn =        {978-4-9906441-0-9},
  book_pages =  {3768},
  month =       {November},
  day =         {11-15},
  venue =       {Tsukuba International Congress Center, Tsukuba, Japan},
  organization ={IAPR},
  annote =      {During the last decade the super-modular Pair-wise
    Markov Networks (SM-PMN) have become a routinely used model for
    structured prediction. Their popularity can be attributed to
    efficient algorithms for the MAP inference. Comparably efficient
    algorithms for learning their parameters from data have not been
    available so far. We propose an instance of the Analytic Center
    Cutting Plane Method (ACCPM) for discriminative learning of the
    SM-PMN from annotated examples. We empirically evaluate the
    proposed ACCPM on a problem of learning the SM-PMN for image
    segmentation. Results obtained on two public datasets show that
    the proposed ACCPM significantly outperforms the current
    state-of-the-art algorithm in terms of computational time as well
    as the accuracy because it can learn models which were not
    tractable by existing methods.},
  keywords =    {Machine Learning and Data Mining, Statistical,
    Syntactic and Structural Pattern Recognition, Segmentation, 
    Color and Texture},
  prestige =    {international},
  project =     {FP7-ICT-247525 HUMAVIPS, PERG04-GA-2008-239455 SEMISOL, Visegrad Scholarship contract No. 51100258},
  psurl =       {Antoniuk-Franc-Hlavac-ICPR-2012, 553 KB},
  www  =        {http://www.icpr2012.org},
  acceptance_ratio = {0.49},