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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01294610
Contributor : Lip6 Publications <>
Submitted on : Tuesday, March 29, 2016 - 3:16:49 PM
Last modification on : Thursday, September 19, 2019 - 2:20:04 PM

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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. pp.265-272, ⟨10.1145/1553374.1553408⟩. ⟨hal-01294610⟩

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