Online learning in discrete hidden Markov models

Alamino, Roberto C. and Caticha, Nestor (2006). Online learning in discrete hidden Markov models. IN: Bayesian inference and maximum entropy methods In science and engineering. Mohammad-Djafari, Ali (ed.) AIP conference proceedings . FRA: AIP.


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.

Publication DOI:
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied Sciences
Additional Information: © 2007 The Authors
Event Title: Bayesian inference and maximum entropy methods In science and engineering
Event Type: Other
Event Dates: 2006-07-08 - 2006-07-13
Uncontrolled Keywords: Bayesian algorithm,generalization error,HMMs,online algorithm,Physics and Astronomy(all)
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://scitatio ... .1063/1.2423274 (Publisher URL)
Published Date: 2006-12-29
Authors: Alamino, Roberto C. ( 0000-0001-8224-2801)
Caticha, Nestor



Version: Accepted Version

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