Improved Learning for Hidden Markov Models Using Penalized Training

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://sro.sussex.ac.uk/id/eprint/1368
Google Scholar:1 Citations
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