@Article{Sonnenburg-SHOGUN-JMLR2010,
  IS = { zkontrolovano 12 Jul 2010 },
  UPDATE  = { 2010-07-12 },
  author =     {Sonnenburg, S{\" o}ren and R{\"a}tsch, Gunnar and
                Henschel, Sebastian and Widmer, Christian and Behr, Jonas and
                Zien, Alexander and de~Bona, Fabio and Binder, Alexander and 
                Gehl, Christian and Franc, Vojt{\v e}ch},
  title =      {The SHOGUN Machine Learning Toolbox},
  year =       {2010},
  month =      {June},
  pages =      {1799-1802},
  journal =    {Journal of Machine Learning Research},
  publisher =  {Microtome Publishing},
  address =    {31 Gibbs st., Brookline, United States},
  issn =       {1532-4435},
  volume =     {11},
  number =     {6},
  authorship = {50-10-5-5-5-5-5-5-5-5},
  annote = {We have developed a machine learning toolbox, called
    SHOGUN, which is designed for unified large-scale learning for a
    broad range of feature types and learning settings. It offers a
    considerable number of machine learning models such as support
    vector machines, hidden Markov models, multiple kernel learning,
    linear discriminant analysis, and more. Most of the specific
    algorithms are able to deal with several different data
    classes. We have used this toolbox in several applications from
    computational biology, some of them coming with no less than 50
    million training examples and others with 7 billion test
    examples. With more than a thousand installations worldwide,
    SHOGUN is already widely adopted in the machine learning community
    and beyond. SHOGUN is implemented in C++ and interfaces to
    MATLABTM, R, Octave, Python, and has a stand-alone command line
    interface. The source code is freely available under the GNU
    General Public License, Version 3 at
    http://www.shogun-toolbox.org.},
  keywords =   {machine learning, support vector machines, kernels, 
                large-scale learning, Python, Octave, R},
  project =    {SEMISOL PERG04-GA-2008-239455},
  psurl =      {[PDF]},
}