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Abstract: The huge popularity of Hidden Markov models in pattern recognition is due to the ability to "learn" model parameters from an observation sequence through Baum-Welch and other re-estimation procedures. In the case of HMM parameter estimation from an ensemble of observation sequences, rather than a single sequence, we require techniques for finding the parameters which maximize the likelihood of the estimated model given the entire set of observation sequences. The importance of this study is... (Update)
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BibTeX entry: (Update)
@misc{ es-improved,
author = "Observation Sequenc Es",
title = "Improved Estimation of Hidden Markov Model Parameters from Multiple Observation
Sequences",
url = "citeseer.ist.psu.edu/717086.html" }
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2 An embedded HMM approach for face detection and recognition (context) - Nefian, Hayes - 1999
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