Bayesian online algorithms for learning in discrete Hidden Markov Models

Roberto C. Alamino; Nestor Caticha

Bayesian online algorithms for learning in discrete Hidden Markov Models

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1-10
Number of pages	10
Journal	Discrete and Dontinuous Dynamical Systems: Series B
Volume	9
Issue number	1
Publication status	Published - Jan 2008

Bibliographical note

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Discrete and Continuous Dynamical Systems Series B following peer review. The definitive publisher-authenticated version Alamino, Roberto C. and Caticha, Nestor (2008) Bayesian online algorithms for learning in Hidden Markov Models. Discrete and Continuous Dynamical Systems Series B , 9 (1). pp. 1-10. ISSN 1531-3492 is available online at: http://aimsciences.org/journals/pdfs.jsp?paperID=2980&mode=abstract

Keywords

Bayesian online algorithms
discrete Hidden Markov Models
Baldi-Chauvin algorithm
Kullback-Leibler divergence
learning curves

Access to Document

BAYESIAN ONLINE ALGORITHMS FOR LEARNING IN DISCRETE HIDDEN MARKOV MODELS
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Discrete and Continuous Dynamical Systems Series B following peer review. The definitive publisher-authenticated version Alamino, Roberto C. and Caticha, Nestor (2008) Bayesian online algorithms for learning in Hidden Markov Models. Discrete and Continuous Dynamical Systems Series B , 9 (1). pp. 1-10. ISSN 1531-3492 is available online at: http://aimsciences.org/journals/pdfs.jsp?paperID=2980&mode=abstract
Accepted author manuscript, 255 KB

Cite this

@article{a19f85f96a83475bac9dab94bbc8dae4,

title = "Bayesian online algorithms for learning in discrete Hidden Markov Models",

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.",

keywords = "Bayesian online algorithms, discrete Hidden Markov Models, Baldi-Chauvin algorithm, Kullback-Leibler divergence, learning curves",

author = "Alamino, {Roberto C.} and Nestor Caticha",

note = "This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Discrete and Continuous Dynamical Systems Series B following peer review. The definitive publisher-authenticated version Alamino, Roberto C. and Caticha, Nestor (2008) Bayesian online algorithms for learning in Hidden Markov Models. Discrete and Continuous Dynamical Systems Series B , 9 (1). pp. 1-10. ISSN 1531-3492 is available online at: http://aimsciences.org/journals/pdfs.jsp?paperID=2980&mode=abstract",

year = "2008",

month = jan,

language = "English",

volume = "9",

pages = "1--10",

journal = "Discrete and Dontinuous Dynamical Systems: Series B",

issn = "1531-3492",

publisher = "Southwest Missouri State University",

number = "1",

}

TY - JOUR

T1 - Bayesian online algorithms for learning in discrete Hidden Markov Models

AU - Alamino, Roberto C.

AU - Caticha, Nestor

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Discrete and Continuous Dynamical Systems Series B following peer review. The definitive publisher-authenticated version Alamino, Roberto C. and Caticha, Nestor (2008) Bayesian online algorithms for learning in Hidden Markov Models. Discrete and Continuous Dynamical Systems Series B , 9 (1). pp. 1-10. ISSN 1531-3492 is available online at: http://aimsciences.org/journals/pdfs.jsp?paperID=2980&mode=abstract

PY - 2008/1

Y1 - 2008/1

N2 - 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.

AB - 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.

KW - Bayesian online algorithms

KW - discrete Hidden Markov Models

KW - Baldi-Chauvin algorithm

KW - Kullback-Leibler divergence

KW - learning curves

UR - http://www.scopus.com/inward/record.url?scp=70350695746&partnerID=8YFLogxK

UR - http://aimsciences.org/journals/pdfs.jsp?paperID=2980&mode=abstract

M3 - Article

SN - 1531-3492

VL - 9

SP - 1

EP - 10

JO - Discrete and Dontinuous Dynamical Systems: Series B

JF - Discrete and Dontinuous Dynamical Systems: Series B

IS - 1

ER -

Bayesian online algorithms for learning in discrete Hidden Markov Models

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this