Bundle Method for Structured Output Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00200618" target="_blank" >RIV/68407700:21230/12:00200618 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Bundle Method for Structured Output Learning

Popis výsledku v původním jazyce

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 or bio-informatics. Learning of the structuredoutput classifiers leads to solving a convex minimization problem which is not tractable by standard algorithms. A significant effort has been put to development of specialized solvers among which the Bundle Method for Risk Minimization (BMRM) [Teo et al., 2010] is one of the most successful. The BMRM is a simplified variant of bundle methods well known in the filed of non-smooth optimization. The simplicity of the BMRM is compensated by its reduced efficiency. In this paper, we propose several improvements of the BMRM which significantly speeds up its convergence. The improvements involve i) using the prox-term known from the original bundle methods, ii) starting optimization from a non-trivial initial solution and iii) using multiple

Název v anglickém jazyce

Bundle Method for Structured Output Learning

Popis výsledku anglicky

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 or bio-informatics. Learning of the structuredoutput classifiers leads to solving a convex minimization problem which is not tractable by standard algorithms. A significant effort has been put to development of specialized solvers among which the Bundle Method for Risk Minimization (BMRM) [Teo et al., 2010] is one of the most successful. The BMRM is a simplified variant of bundle methods well known in the filed of non-smooth optimization. The simplicity of the BMRM is compensated by its reduced efficiency. In this paper, we propose several improvements of the BMRM which significantly speeds up its convergence. The improvements involve i) using the prox-term known from the original bundle methods, ii) starting optimization from a non-trivial initial solution and iii) using multiple

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů