Online Learning in Discrete Hidden Markov Models

Details Online Learning in Discrete Hidden Markov Models Online Learning in Discrete Hidden Markov Models

2007-08-17

arxiv.org/abs/0708.2377v1

Statistics

Machine Learning

8 pages, 6 figures

Scientific paper

10.1063/1.2423274

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.

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