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|Title:||Coupling weight elimination and genetic algorithms||Authors:||Kasparis, Takis
Bebis, George N.
Georgiopoulos, Michael N.
|Keywords:||Functions;Genetic algorithms;Neural networks;Computer networks||Issue Date:||1996||Publisher:||IEEE||Source:||IEEE International Conference on Neural Networks, 1996, Volume 2, Pages 1115-1120||Abstract:||Network size plays an important role in the generalization performance of a network. A number of approaches which try to determine an 'appropriate' network size for a given problem have been developed during the last few years. Although it is usually demonstrated that such approaches are capable of finding small size networks that solve the problem at hand, it is quite remarkable that the generalization capabilities of these networks have not been thoroughly explored. In this paper, we have considered the weight elimination technique and we propose a scheme where it is coupled with genetic algorithms. Our objective is not only to find smaller size networks that solve the problem at hand, by pruning larger size networks, but also to improve generalization. The innovation of our work relies on a fitness function which uses an adaptive parameter to encourage the reproduction of networks having good generalization performance and a relatively small size.||URI:||http://ktisis.cut.ac.cy/handle/10488/7173||ISBN:||0-7803-3210-5||DOI:||10.1109/ICNN.1996.549054||Rights:||© 1996 IEEE||Type:||Book Chapter|
|Appears in Collections:||Κεφάλαια βιβλίων/Book chapters|
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