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. A new method is proposed for selection of the optimal numberof components of a mixture model for pattern classification. We approximatea class-conditional density by a mixture of Gaussian components.We estimate the parameters of the mixture components by the EM(Expectation Maximization) algorithm and select the optimal number ofcomponents on the basis of the MDL (Minimum Description Length)principle. We evaluate the goodness of an estimated model in a tradeoffbetween the number of the...

MDL-based selection of the number of components in mixture models for pattern classification