Waveform Modeling Seyoung Kim University of California,...  Home/Search
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Abstract: This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize waveform shape and shape variation is captured by adding random effects to the segmental model. The resulting probabilistic framework provides a basis for learning of waveform models from data as well as parsing and recognition of new waveforms. Expectation-maximization (EM) algorithms are derived and investigated... (Update)
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BibTeX entry: (Update)
@misc{ seyoung-journal,
author = "Waveform Modeling Seyoung",
title = "Journal of Machine Learning Research 7 (2006) 945--969 Submitted 10/05;
Revised 3/06; Published 10/06 Segmental Hidden Markov Models with Random
Effects for",
url = "citeseer.ist.psu.edu/758762.html" }
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