DocumentCode
1645369
Title
Pruning product unit neural networks
Author
Ismail, A. ; Engelbrecht, A.P.
Author_Institution
Dept. of Comput. Sci., Univ. of Western Cape, South Africa
Volume
1
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
257
Lastpage
262
Abstract
Selection of the optimal architecture of a neural network is crucial to ensure good generalization by reducing the occurrence of overfitting. While much work has been done to develop pruning algorithms for networks that employ summation units, not much has been done on pruning of product unit neural networks. The paper develops and tests a pruning algorithm for product unit networks, and illustrates its performance on several function approximation tasks
Keywords
function approximation; learning (artificial intelligence); mean square error methods; neural net architecture; statistical analysis; function approximation; generalization; optimal architecture; overfitting; product unit neural networks; pruning algorithm; Africa; Application software; Computer architecture; Computer networks; Computer science; Econometrics; Multi-layer neural network; Neural networks; Process planning; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1005479
Filename
1005479
Link To Document