Publications (1)0 Total impact
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Article: Bayesian online algorithms for learning in discrete Hidden Markov Models
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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.
