Incremental Estimation of Discrete Hidden Markov Models Based on a New Backward Procedure

German Florez-Larrahondo, Susan Bridges, Eric A. Hansen

We address the problem of learning discrete hidden Markov models from very long sequences of observations. Incremental versions of the Baum-Welch algorithm that approximate the β-values used in the backward procedure are commonly used for this problem, since their memory complexity is independent of the sequence length. We introduce an improved incremental Baum-Welch algorithm with a new backward procedure that pproximates the β-values based on a one-step lookahead in the training sequence. We justify the new approach analytically, and report empirical results that show it converges faster than previous incremental algorithms.

Subjects: 12. Machine Learning and Discovery; 12.1 Reinforcement Learning

Content Area: 12. Machine Learning

Submitted: May 10, 2005


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