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Improved Classification Using Hidden Markov Averaging From Multiple Observation Sequences

By R. I. A. Davis, C. J. Walder and Brian C. Lovell

Abstract

The enormous popularity of Hidden Markov models (HMMs) in spatio-temporal pattern recognition is largely due to the ability to \u27learn\u27 model parameters from observation sequences through the Baum-Welch and other re-estimation procedures. In this study, HMM parameters are estimated from an ensemble of models trained on individual observation sequences. The proposed methods are shown to provide superior classification performance to competing methods

Topics: HMM, Hidden Markov models, iris-research, 280200 Artificial Intelligence and Signal and Image Processing, 290901 Electrical Engineering, E1, 780101 Mathematical sciences
Publisher: Queensland University of Technology
Year: 2002
OAI identifier: oai:espace.library.uq.edu.au:UQ:11264

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