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This paper presents a new finite mixture model based on a generalization of the Dirichlet distribution. For the estimation of the parameters of this mixture we use a GEM (generalized expectation maximization) algorithm based on a Newton-Raphson step. The experimental results involve the comparison of the performance of Gaussian and generalized Dirichlet mixtures in the classification of several pattern-recognition data sets.
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Volume:1 )
Date of Conference: 23-26 Aug. 2004