Online learning in discrete hidden Markov models

Roberto C. Alamino*, Nestor Caticha

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

We present and analyze three different online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare their performance with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of the generalization error we draw learning curves in simplified situations and compare the results. The performance for learning drifting concepts of one of the presented algorithms is analyzed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented.

Original languageEnglish
Title of host publicationBayesian inference and maximum entropy methods In science and engineering
EditorsAli Mohammad-Djafari
PublisherAIP
Pages187-194
Number of pages8
ISBN (Print)978-0-7354-0371-6
DOIs
Publication statusPublished - 29 Dec 2006
EventBayesian inference and maximum entropy methods In science and engineering - Paris, France
Duration: 8 Jul 200613 Jul 2006

Publication series

NameAIP conference proceedings
PublisherAIP
Volume872
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceBayesian inference and maximum entropy methods In science and engineering
CountryFrance
City Paris
Period8/07/0613/07/06

Bibliographical note

© 2007 The Authors

Keywords

  • Bayesian algorithm
  • generalization error
  • HMMs
  • online algorithm

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  • The evolution of learning systems: to Bayes or not to be

    Caticha, N. & Neirotti, J. P., 29 Dec 2006, Bayesian inference and maximum entropy methods in science and engineering. Mohammad-Djafari, A. (ed.). AIP, p. 203-210 8 p. (AIP conference proceedings; vol. 872).

    Research output: Chapter in Book/Report/Conference proceedingConference publication

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