Incremental estimation of discrete hidden Markov models based on a new backward procedure

Authors:
German Florez-Larrahondo;Susan Bridges;Eric A. Hansen
Affiliations:
Department of Computer Science and Engineering, Mississippi State University, MS;Department of Computer Science and Engineering, Mississippi State University, MS;Department of Computer Science and Engineering, Mississippi State University, MS
Venue:
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Year:
2005

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Abstract

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 approximates 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.