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Improved learning for hidden Markov models using penalized training

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posted on 2023-06-07, 14:09 authored by Bill Keller, Rudi Lutz
In this paper we investigate the performance of penalized variants of the forwards-backwards algorithm for training Hidden Markov Models. Maximum likelihood estimation of model parameters can result in over-fitting and poor generalization ability. We discuss the use of priors to compute maximum a posteriori estimates and describe a number of experiments in which models are trained under different conditions. Our results show that MAP estimation can alleviate over-fitting and help learn better parameter estimates.

History

Publication status

  • Published

Journal

AICS '02: Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science

Publisher

Springer-Verlag

Volume

2464

Page range

153-166

Pages

376.0

Book title

Artificial Intelligence and Cognitive Science

Place of publication

London, UK

ISBN

9783540441847

Series

Lecture Notes in Computer Science

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2008-02-22

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