Statistical formulation of structured output learning from partially annotated examples

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00211711" target="_blank" >RIV/68407700:21230/13:00211711 - isvavai.cz</a>

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Poster2013.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Poster2013.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Statistical formulation of structured output learning from partially annotated examples

Popis výsledku v původním jazyce

Empirical risk minimization based methods for structured output learning have proved successful in real-life applications. A considerable deficiency of existing algorithms, like e.g. the Structured Output SVMs (SO-SVM), is the demand for fully annotatedtraining examples. Despite several recently published works trying to extend SO-SVM for learning from partially annotated examples, two crucial problems remain open: 1) an exact statistical formulation of risk minimization based learning from partially annotated examples and 2) an efficient learning algorithm. While the existing works attempted the algorithmic issues (i.e. the second problem), in this paper we tackle the first problem. In particular, we formulate learning of the structured output classifiers from partially annotated examples as an instance of the expected risk minimization problem. We show that the minimization of the expected risk is equivalent to the minimization of a partial loss which can be evaluated on partially a

Název v anglickém jazyce

Statistical formulation of structured output learning from partially annotated examples

Popis výsledku anglicky

Empirical risk minimization based methods for structured output learning have proved successful in real-life applications. A considerable deficiency of existing algorithms, like e.g. the Structured Output SVMs (SO-SVM), is the demand for fully annotatedtraining examples. Despite several recently published works trying to extend SO-SVM for learning from partially annotated examples, two crucial problems remain open: 1) an exact statistical formulation of risk minimization based learning from partially annotated examples and 2) an efficient learning algorithm. While the existing works attempted the algorithmic issues (i.e. the second problem), in this paper we tackle the first problem. In particular, we formulate learning of the structured output classifiers from partially annotated examples as an instance of the expected risk minimization problem. We show that the minimization of the expected risk is equivalent to the minimization of a partial loss which can be evaluated on partially a

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GAP202%2F12%2F2071" target="_blank" >GAP202/12/2071: Strukturované statistické modely pro porozumění obrazům</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

POSTER 2013 - 17th International Student Conference on Electrical Engineering

ISBN

978-80-01-05242-6

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Czech Technical University

Místo vydání

Prague

Místo konání akce

Prague

Datum konání akce

16. 5. 2013

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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