Online Learning in Discrete Hidden Markov Models

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We 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.
8 pages, 6 figures

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Machine Learning

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http://salesiana.dossiersoluciones.com/handle/123456789/136235

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0708.2377
 http://arxiv.org/abs/0708.2377
 doi:10.1063/1.2423274

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arXiv