DocumentCode
3191264
Title
Pruning strategies for the MTiling constructive learning algorithm
Author
Parekh, Rajesh ; Tang, Ju ; Honavar, Vasant
Author_Institution
Dept. of Comput. Sci., Iowa State Univ., Ames, IA, USA
Volume
3
fYear
1997
fDate
9-12 Jun 1997
Firstpage
1960
Abstract
We present a framework for incorporating pruning strategies in the MTiling constructive neural network learning algorithm. Pruning involves elimination of redundant elements (connection weights or neurons) from a network and is of considerable practical interest. We describe three elementary sensitivity based strategies for pruning neurons. Experimental results demonstrate a moderate to significant reduction in the network size without compromising the network´s generalization performance
Keywords
generalisation (artificial intelligence); learning (artificial intelligence); neural nets; pattern classification; redundancy; sensitivity analysis; MTiling learning algorithm; connection weights; constructive neural network; generalization; network pruning; pattern classification; redundant elements; sensitivity; Algorithm design and analysis; Artificial intelligence; Artificial neural networks; Computer science; Feeds; Learning; Network topology; Neural networks; Neurons; Pattern classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks,1997., International Conference on
Conference_Location
Houston, TX
Print_ISBN
0-7803-4122-8
Type
conf
DOI
10.1109/ICNN.1997.614199
Filename
614199
Link To Document