DocumentCode
1749170
Title
Optimal artificial neural network architecture selection for bagging
Author
Andersen, Tim ; Rimer, Mike ; Martinez, Tony
Author_Institution
Iarchives, Provo, UT, USA
Volume
2
fYear
2001
fDate
2001
Firstpage
790
Abstract
Studies the performance of standard architecture selection strategies, such as cost/performance and CV based strategies, for voting methods such as bagging. It is shown that standard architecture selection strategies are not optimal for voting methods and tend to underestimate the complexity of the optimal network architecture, since they only examine the performance of the network on an individual basis and do not consider the correlation between responses from multiple networks
Keywords
Bayes methods; information theory; learning (artificial intelligence); neural net architecture; pattern classification; CV based strategy; bagging; cost/performance based strategy; optimal artificial neural network architecture selection; voting methods; Artificial neural networks; Bagging; Bayesian methods; Computer architecture; Costs; Diversity reception; Error correction; Neural networks; USA Councils; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939460
Filename
939460
Link To Document