@TechReport{Antoniuk-CAK-2013-48,
  IS = { zkontrolovano 20 Jan 2014 },
  UPDATE  = { 2014-01-20 },
  author =       {Antoniuk, Kostiantyn},
  title =        {Discriminative Methods for Semi-supervised Learning},
  institution =  {Department of Cybernetics, Faculty of Electrical Engineering,
                  Czech Technical University},
  address =      {Prague, Czech Republic},
  year =         {2013},
  month =        {August},
  type =         {Research Report},
  number =       {K333--48/13, CTU--CMP--2013--22},
  project = {1M0567, SGS12/187/OHK3/3T/13, TACR TE01020197},
  pages =        {70},
  figures =      {0},
  authorship =   {100},
  psurl =        {[Antoniuk-TR-2013-22.pdf]},
  annote =       {In this report, we outline the open issues and topics for the work towards the
PhD thesis. The topics we plan to investigate are related to the methods for
discriminative learning from the partially annotated examples. We put emphasis
on the structured output classification where such learning methods are
desperately needed. However, we are fully aware of a very high risk connected with
this topic because the most straightforward ideas have been already exploited by
others without a big success. In order to have a less risky contingency plan we
also intend to investigate some new ideas for "flat" classification with the
hope that they can be further generalized to the SOL. Report contains overview of current 
state-of-the-art methods and our contribution to the field.},
  keywords =     {Learning from partitial annotations, Semi-supervised learning, SVM, S3VM, BMRM, ACCPM, ordinal regression, age recognition, Markov Random Fields},
}