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 9783540441847Full text not available from this repository.
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|
|Google Scholar:||1 Citations|