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

Roberto C. Alamino, Nestor Caticha

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

1 Citation (Scopus)

143 Downloads (Pure)

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 = "American Institute of Mathematical Sciences",

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