A Bee-Inspired Data Clustering Approach to Design RBF Neural Network Classifiers

被引：2

作者：

Ferreira Cruz, Davila Patricia ^{[1
]}

Maia, Renato Dourado ^{[2
]}

da Silva, Leandro Augusto ^{[1
]}

de Castro, Leandro Nunes ^{[1
]}

机构：

[1] Univ Prebiteriana Mackenzie, Grad Program Elect Engn, Nat Comp Lab LCoN, Sao Paulo, Brazil

[2] Univ Estadual Montes Claros, Dept Comp Sci, Claros, MG, Brazil

来源：

DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 11TH INTERNATIONAL CONFERENCE | 2014年 / 290卷

基金：

巴西圣保罗研究基金会;

关键词：

RBF; optimal data clustering; neural network; bee-inspired algorithms;

D O I：

10.1007/978-3-319-07593-8_63

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Different methods have been used to train radial basis function neural networks. This paper proposes a bee-inspired algorithm to automatically select the number and location of basis functions to be used in such RBF network. The algorithm was designed to solve data clustering problems, where the centroids of clusters are used as centers for the RBF network. The approach presented in this paper is preliminary evaluated in three synthetic datasets, two classification datasets and one function approximation problem, and its results suggest a potential for real-world application.

引用

页码：545 / 552

页数：8

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