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ESANN
2004

Flexible and Robust Bayesian Classification by Finite Mixture Models

9 years 11 months ago
Flexible and Robust Bayesian Classification by Finite Mixture Models
Abstract. The regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid commonly faced numerical difficulties encountered with EM. Its principle is applied to Gaussian and Student-t mixtures, resulting in reliable density estimates, the model complexity being kept low. Besides, the regularized models are robust to various noise types. Finally, it is shown that the quality of the associated Bayesian classification is near optimal on Ripley's synthetic data set.
Cédric Archambeau, Frédéric V
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ESANN
Authors Cédric Archambeau, Frédéric Vrins, Michel Verleysen
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