New Radial Basis Function Neural Network Architecture for Pattern Classification: First Results

被引：0

作者：

Sossa, Humberto ^{[1
]}

Cortes, Griselda ^{[2
]}

Guevara, Elizabeth ^{[1
]}

机构：

[1] Inst Politecn Nacl, CIC, Miguel Othon Mendizabal S-N, Mexico City 07738, DF, Mexico

[2] Tecnol Estudios Super Ecatepec, Mexico City, DF, Mexico

来源：

PROGRESS IN PATTERN RECOGNITION IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2014 | 2014年 / 8827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 the initial results concerning a new Radial Basis Function Artificial Neural Network (RBFNN) architecture for pattern classification. Performance of the new architecture is demonstrated with different data sets. Its efficiency is also compared with different classification methods reported in literature: Multilayer Perceptron, Standard Radial Basis Neural Networks, KNN and Minimum Distance classifiers, showing a much better performance. Results are only given for problems using two features.

引用

页码：706 / 713

页数：8

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