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Large margin training for hidden Markov models with partially observed states

Published: 14 June 2009 Publication History

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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ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning
June 2009
1331 pages
ISBN:9781605585161
DOI:10.1145/1553374

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  • NSF
  • Microsoft Research: Microsoft Research
  • MITACS

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2009

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  • Microsoft Research

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Overall Acceptance Rate 140 of 548 submissions, 26%

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