Divergence Based Online Learning in Vector Quantization

被引：0

作者：

Villmann, Thomas ^{[1
]}

Haase, Sven ^{[1
]}

Schleif, Frank-Michael ^{[2
]}

Hammer, Barbara ^{[2
]}

机构：

[1] Univ Appl Sci Mittweida, Dept Math Nat Sci Informat, Mittweida, Germany

[2] Tech Univ Clausthal, Inst Comp Sci, Zellerfeld, Germany

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT I | 2010年 / 6113卷

关键词：

vector quantization; divergence based learning; information theory; MAPS;

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：

We propose the utilization of divergences in gradient descent learning of supervised and unsupervised vector quantization as an alternative for the squared Euclidean distance. The approach is based on the determination of the Frechet-derivatives for the divergences, wich can be immediately plugged into the online-learning rules.

引用

页码：479 / +

页数：3

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