FASOLE: Fast Algorithm for Structured Output LEarning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00223288" target="_blank" >RIV/68407700:21230/14:00223288 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-Fasole-ECML2014.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-Fasole-ECML2014.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-662-44848-9_26" target="_blank" >10.1007/978-3-662-44848-9_26</a>
Alternative languages
Result language
angličtina
Original language name
FASOLE: Fast Algorithm for Structured Output LEarning
Original language description
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 providescertificate 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/LL1303" target="_blank" >LL1303: Large Scale Category Retrieval</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2013, part I
ISBN
978-3-662-44847-2
ISSN
0302-9743
e-ISSN
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Number of pages
16
Pages from-to
402-417
Publisher name
Springer
Place of publication
Heidelberg
Event location
Nancy
Event date
Sep 15, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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