Optimized Learning Vector Quantization Classifier with an Adaptive Euclidean Distance

被引：0
|
作者：
de Souza, Renata M. C. R. [1 ]
Silva Filho, Telmo de M. [1 ]
机构：
[1] Univ Fed Pernambuco, Ctr Informat CIn, BR-50740540 Recife, PE, Brazil
来源：
ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT I | 2009年 / 5768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：
This paper presents a classifier based on Optimized Learning Vector Quantization (optimized version of the basic LVQ1) and an adaptive Euclidean distance. The classifier furnishes discriminative class regions of the input data set that are represented by prototypes. In order to compare prototypes and patterns, the classifier uses an adaptive Euclidean distance that changes at each iteration but is the same for all the class regions. Experiments with real and synthetic data sets demonstrate the usefulness of this classifier.
引用
收藏
页码：799 / 806
页数：8
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