DocumentCode :
3493493
Title :
Rule-extraction from radial basis function networks
Author :
McGarry, Kenneth J. ; Tait, John ; Wermter, Stefan ; MacIntyre, John
Author_Institution :
Sch. of Comput. Eng. & Technol., Univ. of Sunderland, UK
Volume :
2
fYear :
1999
fDate :
1999
Firstpage :
613
Abstract :
Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classification problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris flower classification task and a vibration diagnosis classification task to illustrate the new knowledge extraction techniques. The rules are analyzed in order to gain knowledge and insight into the network representations. We argue that the local Gaussian representation in RBF networks is particularly useful for rule extraction
Keywords :
radial basis function networks; RBF networks; hidden unit conversion; iris flower classification task; knowledge extraction techniques; local Gaussian representation; local representation; pattern classification; pattern recognition; radial basis function networks; symbolic rule extraction; vibration diagnosis classification task;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470)
Conference_Location :
Edinburgh
ISSN :
0537-9989
Print_ISBN :
0-85296-721-7
Type :
conf
DOI :
10.1049/cp:19991178
Filename :
817999
Link To Document :
بازگشت