Large Margin Training for Hidden Markov Models with Partially Observed States

Trinh Minh Tri Do 1 Thierry Artières 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Large margin learning of Continuous Density HMMs with a partially labeled dataset has been extensively studied in the speech and handwriting recognition fields. Yet due to the non-convexity of the optimization problem, previous works usually rely on severe approximations so that it is still an open problem. We propose a new learning algorithm that relies on non-convex optimization and bundle methods and allows tackling the original optimization problem as is. It is proved to converge to a solution with accuracy ε with a rate O (1/ε). We provide experimental results gained on speech and handwriting recognition that demonstrate the potential of the method.
Type de document :
Communication dans un congrès
International Conference on Machine Learning (ICML), Jun 2009, Montreal, Canada. ACM, International Conference on Machine Learning (ICML), pp.265-272, 〈10.1145/1553374.1553408〉
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https://hal.archives-ouvertes.fr/hal-01294610
Contributeur : Lip6 Publications <>
Soumis le : mardi 29 mars 2016 - 15:16:49
Dernière modification le : vendredi 31 août 2018 - 09:25:56

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Trinh Minh Tri Do, Thierry Artières. Large Margin Training for Hidden Markov Models with Partially Observed States. International Conference on Machine Learning (ICML), Jun 2009, Montreal, Canada. ACM, International Conference on Machine Learning (ICML), pp.265-272, 〈10.1145/1553374.1553408〉. 〈hal-01294610〉

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