American Institute of Mathematical Sciences

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January  2008, 9(1): 1-10. doi: 10.3934/dcdsb.2008.9.1

Bayesian online algorithms for learning in discrete hidden Markov models

Roberto C. Alamino 1, and  Nestor Caticha 2,

1. 

Neural Computing Research Group, Aston University, Main Building, Birmingham, B7 4ET, United Kingdom
2. 

Instituto de Física, Universidade de São Paulo, CP 66318, São Paulo, SP, CEP 05389-970, Brazil

Received  August 2006 Revised  September 2007 Published  October 2007

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.
Keywords: HMM, generalization error, online algorithm, Bayesian algorithm..
Mathematics Subject Classification: Primary: 68T05; Secondary: 60J20, 62F1.
Citation: Roberto C. Alamino, Nestor Caticha. Bayesian online algorithms for learning in discrete hidden Markov models. Discrete & Continuous Dynamical Systems - B, 2008, 9 (1) : 1-10. doi: 10.3934/dcdsb.2008.9.1
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