Design of radial basis function networks using decision trees

Author

Yoo, Jae Hung ; Sethi, Ishwar K.

Author_Institution

Dept. of Comput. Eng., YoSu Nat. Fisheries Univ., ChonNam, South Korea

Volume

3

fYear

1995

fDate

Nov/Dec 1995

Firstpage

1269

Abstract

A radial basis function (RBF) neural network is considered as one of the universal input-output functional mapping learning systems. Important issues in designing an efficient RBF neural network are the number of neurons and the shape and location of neurons to define local receptive fields in feature space. This paper presents a solution to these problems using decision tree partitioning. A conversion algorithm from decision tree to RBF neural network is described. Two examples are presented to illustrate the proposed approach

Keywords

decision theory; feedforward neural nets; function approximation; learning (artificial intelligence); pattern classification; performance evaluation; trees (mathematics); decision tree partitioning; feature space; function approximation; input-output functional mapping; learning systems; local receptive fields; pattern classification; performance evaluation; radial basis function networks; Artificial neural networks; Binary trees; Classification tree analysis; Decision trees; Function approximation; Neural networks; Neurons; Pattern classification; Radial basis function networks; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.487338

Filename

487338