Novel mixtures based on the dirichlet distribution: application to data and image classification
作者： Nizar Bouguila , Djemel Ziou , Jean Vaillancourt
关键词: Multinomial distribution 、 Pattern recognition 、 Dirichlet distribution 、 Contextual image classification 、 Generalized Dirichlet distribution 、 Mathematics 、 Dirichlet problem 、 Gaussian process 、 Dirichlet-multinomial distribution 、 Data modeling 、 Artificial intelligence
摘要: The Dirichlet distribution offers high flexibility for modeling data. This paper describes two new mixtures based on this density: the GDD (Generalized Distribution) and MDD (Multinomial mixtures. These will be used to model continuous discrete data, respectively. We propose a method estimating parameters of these performance our is tested by contextual evaluations. In evaluations we compare Gaussian in classification several pattern-recognition data sets apply mixture problem summarizing image databases.
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