Learning mixture models with support vector machines for sequence classification and segmentation

Trinh Minh Tri Do 1 Thierry Artières 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This paper focuses on learning recognition systems able to cope with sequential data for classification and segmentation tasks. It investigates the integration of discriminant power in the learning of generative models, which are usually used for such data. Based on a procedure that transforms a sample data into a generative model, learning is viewed as the selection of efficient component models in a mixture of generative models. This may be done through the learning of a support vector machine. We propose a few kernels for this and report experimental results for classification and segmentation tasks.
Type de document :
Article dans une revue
Pattern Recognition, Elsevier, 2009, 42 (12), pp.3224-3230. 〈10.1016/j.patcog.2008.12.007〉
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https://hal.archives-ouvertes.fr/hal-01172415
Contributeur : Lip6 Publications <>
Soumis le : mardi 7 juillet 2015 - 13:50:29
Dernière modification le : mercredi 21 mars 2018 - 18:58:10

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Trinh Minh Tri Do, Thierry Artières. Learning mixture models with support vector machines for sequence classification and segmentation. Pattern Recognition, Elsevier, 2009, 42 (12), pp.3224-3230. 〈10.1016/j.patcog.2008.12.007〉. 〈hal-01172415〉

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