Abstract
Hidden Markov Models have many applications in signal processing and pattern recognition, but their convergence-based training algorithms are known to suffer from over-sensitivity to the initial random model choice. This paper describes the boundary between regions in which ensemble learning is superior to Rabiner’s multiple-sequence Baum-Welch training method, and proposes techniques for determining the best method in any arbitrary situation. It also studies the suitability of the training methods using the condition number, a recently proposed diagnostic tool for testing the quality of the model. A new method for training Hidden Markov Models called the Viterbi Path Counting algorithm is introduced and is found to produce significantly better performance than current methods in a range of trials.
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An erratum to this article can be found at http://dx.doi.org/10.1007/s10044-004-0216-3
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Davis, R.I.A., Lovell, B.C. Comparing and evaluating HMM ensemble training algorithms using train and test and condition number criteria. Formal Pattern Analysis & Applications 6, 327–335 (2004). https://doi.org/10.1007/s10044-003-0198-6
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DOI: https://doi.org/10.1007/s10044-003-0198-6