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SEMISOL

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Semi supervised Structured Output Learning from Partially Labeled Data

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 interde... ver más

31/05/2012

CVUT

45K€

Presupuesto del proyecto: 45K€

Líder del proyecto

CESKE VYSOKE UCENI TECHNICKE V PRAZE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2012-05-31 No tenemos la información de la convocatoria

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1 Participantes

CVUT

0.00€ | Lider

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Líder del proyecto

CESKE VYSOKE UCENI TECHNICKE V PRAZE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 45K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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