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
Full text not available from this repository.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 |
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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 |