Artículos de revistas
Bayesian online algorithms for learning in discrete Hidden Markov Models
Fecha
2008Registro en:
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, v.9, n.1, p.1-10, 2008
1531-3492
Autor
ALAMINO, Roberto C.
Caticha, Nestor
Institución
Resumen
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.