Title :
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
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.857879
