IS = { zkontrolovano 28 May 2012 },
  UPDATE      = { 2011-11-13 },
  author =      {Franc, Vojt{\v e}ch and Sonnenburg, S{\" o}ren and 
                 Werner, Tom{\' a}{\v s}},
  title =       {Cutting-Plane Methods in Machine Learning},
  book_title =  {Optimization for Machine Learning},
  year =        {2012},
  pages =       {185--218},
  chapter =     {7},
  editor =      {Sra, Suvrit and Nowozin, Sebastian and Wright, J. Stephen},
  publisher =   {The MIT Press},
  address =     {Cambridge,USA},
  isbn =        {978-0-262-01646-9},
  book_pages =  {494},
  authorship =  {34-33-33},
  annote =      {Cutting plane methods are optimization techniques
    that incrementally construct an approximation of a feasible set or
    an objective function by linear inequalities, called cutting
    planes. Numerous variants of this basic idea are among standard
    tools used in convex nonsmooth optimization and integer linear
    programing.  Recently, cutting plane methods have seen growing
    interest in the field of machine learning.  In this chapter, we
    describe the basic theory behind these methods and we show three
    of their successful applications to solving machine learning
    problems: regularized risk minimization, multiple kernel learning,
    and MAP inference in graphical models.  },
  keywords =    {cutting plane algorithm, Bundle methods, Multiple kernel learning, 
                 MAP inference in graphical models},
  project =     {1M0567, FP7-ICT-247525 HUMAVIPS only EU, PERG04-GA-20080239455 SEMISOL, 
                 EC215078 DIPLECS only EU, MSM6840770038},
  psurl       = {[PDF },