A general framework for symbol and rule extraction in neural networks

Author

Apolloni, Bruno ; Orovas, C. ; Taylor, J. ; Fellenz, W. ; Gielen, Stan ; Westerdijk, Machiel

Author_Institution

Dept. of Comput. Sci., Milan Univ., Italy

Volume

2

fYear

2000

fDate

2000

Firstpage

87

Abstract

We split the rule extraction task into a subsymbolic and a symbolic phase and present a set of neural networks for filling the former. Under the two general commitments of: (i) having a learning algorithm that is sensitive to feedback signals coming from the latter phase, and (ii) extracting Boolean variables whose meaning is determined by the further symbolic processing, we introduce three unsupervised learning algorithms and show related numerical examples for a multilayer perceptron, recurrent neural networks, and a specially devised vector quantizer

Keywords

feedback; multilayer perceptrons; recurrent neural nets; unsupervised learning; vector quantisation; Boolean variables; feedback signals; multilayer perceptron; recurrent neural networks; rule extraction; symbol extraction; unsupervised learning algorithms; vector quantizer; Artificial intelligence; Artificial neural networks; Biological neural networks; Computer science; Data mining; Educational institutions; Filling; Intelligent networks; Mathematics; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.857879

Filename

857879