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• W2966183473 abstract "Mixture models play a crucial role in pattern recognition methods based on clustering. In this paper, we propose a finite generalized inverted Dirichlet mixture model with a variational learning method for parameter estimation. The highlight of our model is the component splitting approach which handles the problem of model selection in an incremental fashion within the variational framework. Efficiency of proposed model is tested for image categorization tasks." @default.
• W2966183473 created "2019-08-13" @default.
• W2966183473 creator A5003895852 @default.
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• W2966183473 date "2019-06-01" @default.
• W2966183473 modified "2023-09-27" @default.
• W2966183473 title "Variational Learning for Finite Generalized Inverted Dirichlet Mixture Models with a Component Splitting Approach" @default.
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• W2966183473 doi "https://doi.org/10.1109/isie.2019.8781300" @default.
• W2966183473 hasPublicationYear "2019" @default.
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