@inproceedings{Uricar-Franc-Hlavac-SCIA-2013,
  IS = { zkontrolovano 24 Jan 2014 },
  UPDATE  = { 2013-08-02 },
   editor    = {K\"{a}m\"{a}r\"{a}inen, Joni-Kristian and Koskela, Markus},
   booktitle = {SCIA 2013: Proceedings of the 18th Scandinavian Conference on Image Analysis},
   publisher = {Springer},
   location  = {Heidelberg},
   series    = {Lecture Notes in Computer Science},
   volume    = {7944},
   year      = {2013},
   address   = {Heidelberg, Germany},
   month     = {June},
   day       = {17--20},
   venue     = {Espoo, Finland},
   isbn      = {978-3-642-38885-9},
   doi       = {10.1007/978-3-642-38886-6_16},
   author    = {U{\v{r}}i{\v{c}}{\'{a}}{\v{r}}, Michal and Franc, Vojt{\v{e}}ch and 
                Hlav{\'{a}}{\v{c}}, V{\'{a}}clav},
   title     = {{Bundle Methods for Structured Output Learning --- Back to the Roots}},
   annote    = {Discriminative methods for learning structured output
     classifiers have been gaining popularity in recent years due to
     their successful applications in fields like computer vision,
     natural language processing, etc. Learning of the structured
     output classifiers leads to solving a convex minimization
     problem, still hard to solve by standard algorithms in real-life
     settings. A significant effort has been put to development of
     specialized solvers among which the Bundle Method for Risk
     Minimization (BMRM) [1] is one of the most successful. The BMRM
     is a simplified variant of bundle methods well known in the filed
     of non-smooth optimization. In this paper, we propose two
     speed-up improvements of the BMRM: i) using the adaptive
     prox-term known from the original bundle methods, ii) starting
     optimization from a non-trivial initial solution. We combine both
     improvements with the multiple cutting plane model approximation
     [2]. Experiments on real-life data show consistently faster
     convergence achieving speedup up to factor of 9.7.},
   keywords  = {Structured Output Learning, Bundle Methods, Risk Minimization, Structured Output SVM},
   authorship= {45-45-10},
   pages     = {162--171},
   book_pages= {733},
   www       = {http://link.springer.com/chapter/10.1007/978-3-642-38886-6_16},
   prestige  = {important},
   project   = {TACR TE01020197, GACR P202/12/2071, FP7-ICT-247525 HUMAVIPS, FP7-288553 CloPeMa},
   organization = { Aalto University, Finland },
   acceptance_ratio = {0.507},
   psurl     = {PDF},
}