IS = { zkontrolovano 15 Jan 2014 },
  UPDATE  = { 2013-09-25 },
  isbn = {978-3-642-40993-6},
  booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2013, part III},
  volume = {8190},
  series = {Lecture Notes in Computer Science},
  editor = {Blockeel, Hendrik and Kersting, Kristian and 
            Nijssen, Siegfried and {\v Z}elezn{\' y}, Filip},
  doi = {10.1007/978-3-642-40994-3_7},
  title = {MORD: Multi-class Classifier for Ordinal Regression},
  url = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Franc-Hlavac-ECML-2013.pdf},
  psurl = {Antoniuk-Franc-Hlavac-ECML-2013, 324 KB},
  publisher = {Springer},
  author = {Antoniuk, Kostiantyn and Franc, Vojt{\v e}ch and Hlav{\'a}{\v c}, V{\'a}clav},
  book_pages = {689},
  pages = {96-111},
  day = {23--27},
  month =   {September},
  year =    {2013},
  project = {SGS12/187/OHK3/3T/13, GACR P202/12/2071, FP7-288553 CloPeMa, 
             Visegrad Scholarship contract No. 51200430},
  address = {Heidelberg, Germany},
  venue = {Prague, Czech Republic},
  keywords = {Ordinal regression; linear multi-class classification},
  annote = { We show that classification rules used in ordinal
    regression are equivalent to a certain class of linear multi-class
    classifiers. This observation not only allows to design new
    learning algorithms for ordinal regression using existing methods
    for multi-class classification but it also allows to derive new
    models for ordinal regression. For example, one can convert
    learning of ordinal classifier with (almost) arbitrary loss
    function to a convex unconstrained risk minimization problem for
    which many efficient solvers exist. The established equivalence
    also allows to increase discriminative power of the ordinal
    classifier without need to use kernels by introducing a piece-wise
    ordinal classifier. We demonstrate advantages of the proposed
    models on standard benchmarks as well as in solving a real-life
    problem. In particular, we show that the proposed piece-wise
    ordinal classifier applied to visual age estimation outperforms
    other standard prediction models.  },