Decompositional Rules Extraction Methods from Neural Networks

Mendel 2010 : 16th International Conference on Soft Computing 2010
Andrejs Bondarenko, Tatjana Zmanovska, Arkādijs Borisovs

Given paper is a review on existing decompositional rules extraction methods from artificial neural networks of several types: feed-forward network, radial basis functions network, second order reccurent network, generalized relevance learning vector quantization and finally support vector machine. Descriptions of all rules extraction methods are containing details on method itself, type of rules extracted, applicable problems and some test results.

Atslēgas vārdi
rule extraction, neural network, pruning, support vector machine, reccurent network, RBF network.

Bondarenko, A., Zmanovska, T., Borisovs, A. Decompositional Rules Extraction Methods from Neural Networks. No: Mendel 2010 : 16th International Conference on Soft Computing, Čehija, Brno, 23.-25. jūnijs, 2010. Brno: University of Technology, 2010, 256.-262.lpp. ISBN 9788021441200.

Publikācijas valoda
English (en)