@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. }, }