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The three steps (estimation, classification, and maximization) are repeated until a stopping criterion is satisfied.
Here, we integrate the constrained clustering method, which was proposed by Lange et al. (2005) and Lu and Leen (2005) into a mixture estimation classification framework, where mixture components are linear HMMs (Lin et al., 2008; Schliep et al., 2003, 2004, 2005).
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In the comparison between the results obtained by the estimation and classification approaches, in the classification analysis approach, the training set mean error (corresponding to the minimum test error) is slightly inferior to the estimation mean error.
Last but not least, note that the idea of employing mixture estimation for classification tasks allowed us to flexibly perform sub-classification, which was of particular use here.
Two different single-channel C 50 estimators are employed in this work: non-intrusive room acoustic estimation using classification and regression trees (NIRA-CART) and non-intrusive room acoustic estimation using bidirectional long-short term memory (NIRA-BLSTM).
In MSRI Workshop on Nonlinear Estimation and Classification, Berkeley, CA, Mar.
These questions are answered by designing estimation and classification experiments using real 6-channel ERPs.
On the anomaly detection side, a component library has been developed for Hidden Markov and Mixture Model parameter estimation and classification.
In D.D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick and B. Yu (eds)., Nonlinear Estimation and Classification, Springer-Verlag, New York, 453-466.
Models of one-reason decision making have been designed for various tasks, including choice, numerical estimation, and classification.
Further applications in maximum likelihood estimation and classification such as optical character recognition are now given within.
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