Sensitivity based Growing and Pruning method for RBF network in online learning environments

Author

Chan, Patrick P K ; Wu, Xi-Rong ; Ng, Wing W Y ; Yeung, Daniel S.

Author_Institution

Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China

Volume

3

fYear

2011

fDate

10-13 July 2011

Firstpage

1107

Lastpage

1112

Abstract

How to define the architecture of classifiers dynamically is one of the major research topics in online learning. This paper presents a new online learning algorithm for Radial Basis Function Network named Sensitivity Based Neurons Growing and Pruning Method for RBF network (SBGAP). The performance of SBGAP is evaluated experimentally by comparing accuracy and the number of neurons with the existing methods. The experimental results show that SBGAP achieve litter higher accuracy with fewer hidden units in most situations.

Keywords

learning (artificial intelligence); pattern classification; radial basis function networks; RBF network; SBGAP; classifier architecture; online learning algorithm; pruning method; radial basis function network; sensitivity based neuron growing; Accuracy; Heart; Machine learning; Neurons; Radial basis function networks; Sensitivity; Training; Decouple Extended Kalman Filter (DEKF); L-GEM; SBGAP; Sensitivity;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016934

Filename

6016934