Statistical formulation of structured output learning from partially annotated examples
The result's identifiers
Result code in 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>
Result on the web
<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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Alternative languages
Result language
angličtina
Original language name
Statistical formulation of structured output learning from partially annotated examples
Original language description
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
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/GAP202%2F12%2F2071" target="_blank" >GAP202/12/2071: Structured Statistical Models for Image Understanding</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
5
Pages from-to
1-5
Publisher name
Czech Technical University
Place of publication
Prague
Event location
Prague
Event date
May 16, 2013
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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