@Inproceedings{Franc-ECML-2013,
  IS = { zkontrolovano 02 Jan 2015 },
  UPDATE  = { 2014-12-19 },
  isbn       = {978-3-662-44847-2},
  booktitle  = {Machine Learning and Knowledge Discovery in Databases
                  - ECML PKDD 2013, part I},
  series     = {Lecture Notes in Computer Science},
  volume = {8724},
  editor     = {Toon Calders and Florian Esposito and Eyke H{\" u}llermeier and Rosa Meo},
  doi        = {10.1007/978-3-662-44848-9_26},
  title      = {FASOLE: Fast Algorithm for Structured Output LEarning},
  url        = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-Fasole-ECML2014.pdf},
  publisher  = {Springer},
  author     = {Franc, Vojt{\v e}ch},
  affiliation = {13133},
  book_pages = {709},
  pages      = {402--417},
  day        = {15--19},
  month      = {September},
  year       = {2014},
  project    = {ERC-CZ LL1303},
  address    = {Heidelberg, Germany},
  venue      = {Nancy, France},
  keywords   = {Structured Output Learning, Convex optimization, Dual coordinate ascent},
  annote     = { 
  This paper proposes a novel Fast Algorithm for Structured Ouput LEarning
  (FASOLE). FASOLE implements the dual coordinate ascent (DCA) algorithm for
  solving the dual problem of the Structured Output Support Vector Machines
  (SO-SVM). Unlike existing instances of DCA algorithm applied for SO-SVM, the
  proposed FASOLE uses a different working set selection strategy which provides
  nearly maximal improvement of the objective function in each update. FASOLE
  processes examples in on-line fashion and it provides certificate of
  optimality. FASOLE is guaranteed to find the {$veps$}-optimal solution in 
  {$SO(frac{1}{veps^2})$} time in the worst case. In the empirical comparison 
  FASOLE consistently outperforms the existing
  state-of-the-art solvers, like the Cutting Plane Algorithm or the
  Block-Coordinate Frank-Wolfe algorithm, achieving up to an order of magnitude
  speedups while obtaining the same precise solution.
  },
}