@TechReport{Franc-TR-2012-16,
  IS = { zkontrolovano 15 Jan 2013 },
  UPDATE  = { 2012-10-01 },
author =      {Franc, Vojt{\v e}ch and
               Antoniuk, Konstantyn and
               U{\v r}i{\v c}{\'a}{\v r}, Michal},
title =       {Discriminative structured output learning from partially
               annotated  examples},
institution = {Center for Machine Perception, K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2012},
month =       {July},
type =        {Research Report},
number =      {CTU--CMP--2012--16},
issn =        {1213-2365},
pages =       {18},
figures =     {2},
authorship =  {70-15-15},
psurl       = {[Franc-TR-2012-16.pdf]},
project =     {PERG04-GA-2008-239455 SEMISOL},
annote =      {The discriminative structured output learning has been
    proved successful in solving many real-life applications. A big
    deficiency of existing algorithms like the Structured Output SVMs
    is the requirement of fully annotated training examples. In this
    report we formulate a problem of learning the structured output
    classifiers from partially annotated examples as an instance of
    the expected risk minimization.  We show that the minimization of
    the expected risk is equivalent to the minimization of the partial
    loss which can be evaluated on partially annotated examples. We
    proposed an instance of the partial learning algorithm for the
    class of linear structured output classifiers which we call
    Partial-SO-SVM. The Partial-SO-SVM algorithm leads to a hard
    non-convex optimization problem. We provide an algorithm solving
    the Partial-SO-SVM problem approximately using an additional prior
    knowledge about the problem. We demonstrated effectiveness of the
    proposed method on two real life computer vision problems, namely,
    the face landmark detection and the image segmentation.},
keywords =    {structured output learning, partially annotated data},
}