Abstract
A new method is proposed for selection of the optimal number of components of a mixture model for pattern classification. We approximate a 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 of components on the basis of the MDL (Minimum Description Length) principle. We evaluate the goodness of an estimated model in a trade-off between the number of the misclassified training samples and the complexity of the model.
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© 1998 Springer-Verlag Berlin Heidelberg
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Tenmoto, H., Kudo, M., Shimbo, M. (1998). MDL-based selection of the number of components in mixture models for pattern classification. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033308
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DOI: https://doi.org/10.1007/BFb0033308
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