Online learning in discrete hidden Markov models

Roberto C. Alamino, Nestor Caticha

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

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

Fingerprint

learning
learning curves
broken symmetry
divergence

Bibliographical note

© 2007 The Authors

Keywords

  • Bayesian algorithm
  • generalization error
  • HMMs
  • online algorithm

Cite this

Alamino, R. C., & Caticha, N. (2006). Online learning in discrete hidden Markov models. In A. Mohammad-Djafari (Ed.), Bayesian inference and maximum entropy methods In science and engineering (pp. 187-194). (AIP conference proceedings; Vol. 872). AIP. https://doi.org/10.1063/1.2423274
Alamino, Roberto C. ; Caticha, Nestor. / Online learning in discrete hidden Markov models. Bayesian inference and maximum entropy methods In science and engineering. editor / Ali Mohammad-Djafari. AIP, 2006. pp. 187-194 (AIP conference proceedings).
@inproceedings{46c8a4a27eb84c10b7294c50f7bd2458,
title = "Online learning in discrete hidden Markov models",
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.",
keywords = "Bayesian algorithm, generalization error, HMMs, online algorithm",
author = "Alamino, {Roberto C.} and Nestor Caticha",
note = "{\circledC} 2007 The Authors",
year = "2006",
month = "12",
day = "29",
doi = "10.1063/1.2423274",
language = "English",
isbn = "978-0-7354-0371-6",
series = "AIP conference proceedings",
publisher = "AIP",
pages = "187--194",
editor = "Ali Mohammad-Djafari",
booktitle = "Bayesian inference and maximum entropy methods In science and engineering",

}

Alamino, RC & Caticha, N 2006, Online learning in discrete hidden Markov models. in A Mohammad-Djafari (ed.), Bayesian inference and maximum entropy methods In science and engineering. AIP conference proceedings, vol. 872, AIP, pp. 187-194, Bayesian inference and maximum entropy methods In science and engineering, Paris, France, 8/07/06. https://doi.org/10.1063/1.2423274

Online learning in discrete hidden Markov models. / Alamino, Roberto C.; Caticha, Nestor.

Bayesian inference and maximum entropy methods In science and engineering. ed. / Ali Mohammad-Djafari. AIP, 2006. p. 187-194 (AIP conference proceedings; Vol. 872).

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

TY - GEN

T1 - Online learning in discrete hidden Markov models

AU - Alamino, Roberto C.

AU - Caticha, Nestor

N1 - © 2007 The Authors

PY - 2006/12/29

Y1 - 2006/12/29

N2 - 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.

AB - 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.

KW - Bayesian algorithm

KW - generalization error

KW - HMMs

KW - online algorithm

UR - http://www.scopus.com/inward/record.url?scp=33845739779&partnerID=8YFLogxK

UR - http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.2423274

U2 - 10.1063/1.2423274

DO - 10.1063/1.2423274

M3 - Conference contribution

SN - 978-0-7354-0371-6

T3 - AIP conference proceedings

SP - 187

EP - 194

BT - Bayesian inference and maximum entropy methods In science and engineering

A2 - Mohammad-Djafari, Ali

PB - AIP

ER -

Alamino RC, Caticha N. Online learning in discrete hidden Markov models. In Mohammad-Djafari A, editor, Bayesian inference and maximum entropy methods In science and engineering. AIP. 2006. p. 187-194. (AIP conference proceedings). https://doi.org/10.1063/1.2423274