On fitting finite Dirichlet mixture using ECM and MML

被引：0
|
作者：
Bouguila, N [1 ]
Ziou, D [1 ]
机构：
[1] Univ Sherbrooke, Sherbrooke, PQ J1K 2R1, Canada
来源：
PATTERN RECOGNITION AND DATA MINING, PT 1, PROCEEDINGS | 2005年 / 3686卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Gaussian mixture models are being increasingly used in pattern recognition applications. However, for a set of data other distributions can give better results. In this paper, we consider Dirichlet mixtures which offer many advantages [1]. The use of the ECM algorithm and the minimum message length (MML) approach to fit this mixture model is described. Experimental results involve the summarization of texture image databases.
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收藏
页码：172 / 182
页数：11
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