DocumentCode
328240
Title
An empirical comparison of node pruning methods for layered feedforward neural networks
Author
Castellano, Giovanna ; Fanelli, Anna Maria ; Pelillo, Marcello
Author_Institution
Dipartimento di Inf., Bari Univ., Italy
Volume
1
fYear
1993
fDate
25-29 Oct. 1993
Firstpage
321
Abstract
One popular approach to reduce the size of an artificial neural network is to prune off the hidden units after learning has taken place. This paper compares three different node pruning algorithms in terms of size and performance of the reduced network. Experimental results are reported and some useful conclusions are drawn.
Keywords
feedforward neural nets; learning (artificial intelligence); performance evaluation; redundancy; hidden units; layered feedforward neural networks; learning; node pruning methods; redundant units; Artificial neural networks; Feedforward neural networks; Feedforward systems; Neural networks; Petroleum; Scattering; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN
0-7803-1421-2
Type
conf
DOI
10.1109/IJCNN.1993.713922
Filename
713922
Link To Document