IS = { zkontrolovano 15 Jan 2014 },
  UPDATE  = { 2013-08-02 },
  title =   {Statistical formulation of structured output learning from partially annotated examples},
  author =  {Antoniuk, Kostiantyn},
  booktitle = {17th International Student Conference on Electrical Engineering},
  pages =   {1--5},
  book_pages= {705},
  address = {Technick\'a 2, Prague, Czech Republic},
  isbn =    {978-80-01-05242-6},
  day =     {16},
  month =   {May},
  year =    {2013},
  publisher = {Czech Technical University in Prague},
  venue = {Praha, Czech Republic},
  annote = { Empirical risk minimization based methods for structured
     output learning have proved successful in real-life
     applications. A considerable deficiency of existing algorithms,
     like e.g. the Structured Output SVMs (SO-SVM), is the demand for
     fully annotated training examples. Despite several recently
     published works trying to extend SO-SVM for learning from
     partially annotated examples, two crucial problems remain open:
     1) an exact statistical formulation of risk minimization based
     learning from partially annotated examples and 2) an efficient
     learning algorithm. While the existing works attempted the
     algorithmic issues (i.e. the second problem), in this paper we
     tackle the first problem. In particular, we formulate learning of
     the structured output classifiers from partially annotated
     examples as an instance of the expected risk minimization
     problem. We show that the minimization of the expected risk is
     equivalent to the minimization of a partial loss which can be
     evaluated on partially annotated examples only. Thus, the
     empirical risk minimization based methods can be applied.  },
  keywords = {Partially annotated examples, structured output learning, risk minimization.},
  project = {SGS12/187/OHK3/3T/13, GACR P202/12/2071, Visegrad Scholarship contract No. 51200430},
  psurl = {Antoniuk-Poster-2013, 204 KB},
  url = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Poster2013.pdf},
  note  = {CD-ROM, IC04},