Keller, Bill and Lutz, Rudi (2002) Improved Learning for Hidden Markov Models Using Penalized Training. In: Artificial Intelligence and Cognitive Science. Lecture Notes in Computer Science, 2464 . Springer-Verlag, London, UK, pp. 153-166. ISBN 9783540441847

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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.

Item Type:	Book Section
Schools and Departments:	School of Engineering and Informatics > Informatics
Subjects:	Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Depositing User:	Chris Keene
Date Deposited:	22 Feb 2008
Last Modified:	30 Nov 2012 16:51
URI:	http://srodev.sussex.ac.uk/id/eprint/1368
Google Scholar:	1 Citations

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