A powreful finite mixture model based on the generalized Dirichlet distribution: Unsupervised learning and applications

被引：37

作者：

Bouguila, N ^{[1
]}

Ziou, D ^{[1
]}

机构：

[1] Univ Sherbrooke, Sherbrooke, PQ J1K 2R1, Canada

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1 | 2004年

关键词：

D O I：

10.1109/ICPR.2004.1334107

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：280 / 283

页数：4

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