Improved Classification Using Hidden Markov Averaging from Multiple Observation Sequences
نویسندگان
چکیده
The enormous popularity of Hidden Markov models (HMMs) in spatio-temporal pattern recognition is largely due to the ability to “learn” 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.
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