Improved Learning for Hidden Markov Models Using Penalized Training

Authors:
Bill Keller;Rudi Lutz
Affiliations:
-;-
Venue:
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Year:
2002

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Abstract

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.