SEMISOL

Semi-supervised Structured Output Learning from Partially Labeled Data

Coordinatore	CESKE VYSOKE UCENI TECHNICKE V PRAZE    more  Organization address address: ZIKOVA 4 city: PRAHA postcode: 166 36 contact info Titolo: Mr. Nome: Igor Cognome: Mraz Email: send email Telefono: 420224000000 Fax: 420224000000
Nazionalità Coordinatore	Czech Republic [CZ]
Totale costo	45˙000 €
EC contributo	45˙000 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-ERG-2008
Funding Scheme	MC-ERG
Anno di inizio	2009
Periodo (anno-mese-giorno)	2009-06-01   -   2012-05-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	CESKE VYSOKE UCENI TECHNICKE V PRAZE  Organization address address: ZIKOVA 4 city: PRAHA postcode: 166 36 contact info Titolo: Mr. Nome: Igor Cognome: Mraz Email: send email Telefono: 420224000000 Fax: 420224000000	CZ (PRAHA)	coordinator	45˙000.00

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 Obiettivo del progetto (Objective)

'Learning classifiers automatically from examples is subject to the multidisciplinary field of machine learning.

The structured output learning (SOL) is concerned with the learning of classifiers for prediction of multiple interdependent variables exhibiting some structure dependence. Recent progress in SOL focuses mainly on supervised methods that require labeled examples. A high cost of labeled examples significantly limits application of SOL to many domains.

Our goal is threefold. First, to developed framework for semi-supervised SOL from cheap partially labeled examples. Second, to apply this new framework to two important SOL tasks: (i) Markov Networks learning and (ii) learning of 2-dimensional image grammars. Third, to use the new algorithms for solving computer vision problems including the image segmentation and the car license plate recognition.

To achieve the first goal, we will examine two strategies. First, we will combine powerful discriminative methods for SOL with generative models offering a principled way to deal with missing labels. Second, we will extend the existing semi-supervised methods in order to handle the partially labeled examples.

To achieve the second goal, we will incorporate the existing methods for supervised SOL of Markov Networks and 2D grammars to the framework developed as the first goal.

To achieve the third goal, we will build on the technology for image segmentation and license plate recognition developed by the host. The currently used classification methods will be replaced by the developed semi-supervised SOL algorithms to demonstrate their effectiveness on real-life problems.

Achieving the goals will be possible by joining the expertise of the applicant and the host. This applies both to theoretical and application oriented goals. The applicant is experienced in SOL and Markov Networks while the host will complement this with a large expertise in 2D grammars and computer vision.'