Comparing and Evaluating HMM Ensemble Training Algorithms Using Train and Test and Condition Number Criteria. (2004)
| Venue: | Pattern Analysis and Applications |
| Citations: | 3 - 1 self |
BibTeX
@MISC{Davis04comparingand,
author = {Richard I. A. Davis and Brian C. Lovell},
title = {Comparing and Evaluating HMM Ensemble Training Algorithms Using Train and Test and Condition Number Criteria.},
year = {2004}
}
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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 test-ing the quality of the model. A new method for training Hidden Markov Models Correspondence to:
