About: Statistical formulation of structured output learning from partially annotated examples

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	ftp://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Poster2013.pdf
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 annotated training 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 annotated examples only. Thus, the empirical risk minimization based methods can be applied. 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 annotated training 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 annotated examples only. Thus, the empirical risk minimization based methods can be applied. (en)
Title	Statistical formulation of structured output learning from partially annotated examples Statistical formulation of structured output learning from partially annotated examples (en)
skos:prefLabel	Statistical formulation of structured output learning from partially annotated examples Statistical formulation of structured output learning from partially annotated examples (en)
skos:notation	RIV/68407700:21230/13:00211711!RIV14-MSM-21230___
http://linked.open...avai/predkladatel	Fakulta elektrotechnická
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(GAP202/12/2071), S
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Antoniuk, Kostiantyn
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	107773
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/13:00211711
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Partially annotated examples; structured output learning; risk minimization (en)
http://linked.open.../riv/klicoveSlovo	Partially annotated examples risk minimization structured output learning
http://linked.open...ontrolniKodProRIV	[BB27FF6546BB]
http://linked.open...v/mistoKonaniAkce	Prague
http://linked.open...i/riv/mistoVydani	Prague
http://linked.open...i/riv/nazevZdroje	POSTER 2013 - 17th International Student Conference on Electrical Engineering
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	1 (xsd:int)
http://linked.open...vavai/riv/projekt	Strukturované statistické modely pro porozumění obrazům
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Antoniuk, Kostiantyn
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2013-05-16 (xsd:date)
number of pages	5 (xsd:int)
http://purl.org/ne...btex#hasPublisher	České vysoké učení technické v Praze
https://schema.org/isbn	978-80-01-05242-6
http://localhost/t...ganizacniJednotka	21230

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