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A Concise Information-Theoretic Derivation of the Baum-Welch algorithm (1406.7002v1)
Published 24 Jun 2014 in cs.IT, cs.LG, and math.IT
Abstract: We derive the Baum-Welch algorithm for hidden Markov models (HMMs) through an information-theoretical approach using cross-entropy instead of the Lagrange multiplier approach which is universal in machine learning literature. The proposed approach provides a more concise derivation of the Baum-Welch method and naturally generalizes to multiple observations.
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