Online Learning in Discrete Hidden Markov Models

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dc.creatorAlamino, Roberto C.
dc.creatorCaticha, Nestor
dc.date2007-08-17
dc.date.accessioned2026-07-07T08:24:07Z
dc.date.available2026-07-07T08:24:07Z
dc.descriptionWe present and analyse three online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare them with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalisation error we draw learning curves in simplified situations. The performance for learning drifting concepts of one of the presented algorithms is analysed 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.
dc.description8 pages, 6 figures
dc.identifierhttps://arxiv.org/abs/0708.2377
dc.identifierhttp://arxiv.org/abs/0708.2377
dc.identifierdoi:10.1063/1.2423274
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/136235
dc.subjectMachine Learning
dc.titleOnline Learning in Discrete Hidden Markov Models
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