Bayesian online algorithms for learning in discrete hidden Markov models

Roberto C. Alamino; Nestor Caticha

doi:10.3934/dcdsb.2008.9.1

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2008, Volume 9, Issue 1: 1-10. Doi: 10.3934/dcdsb.2008.9.1

This issue Previous Article Next Article Estimates of the wake for the 3D Oseen equations

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
2.
Instituto de Física, Universidade de São Paulo, CP 66318, São Paulo, SP, CEP 05389-970

Received: August 2006

Revised: September 2007

Published: January 2008

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Abstract

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.

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Mathematics Subject Classification: Primary: 68T05; Secondary: 60J20, 62F15.

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