Keywords
architecture selection, cross validation, artificial neural network
Abstract
This paper 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.
Original Publication Citation
Andersen, T. L. and Martinez, T. R., "Optimal Artificial Neural Network Architecture Selection for Bagging", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'1, pp. 79-795, 21.
BYU ScholarsArchive Citation
Andersen, Timothy L.; Martinez, Tony R.; and Rimer, Michael E., "Optimal Artificial Neural Network Architecture Selection for Bagging" (2001). Faculty Publications. 1091.
https://scholarsarchive.byu.edu/facpub/1091
Document Type
Peer-Reviewed Article
Publication Date
2001-07-19
Permanent URL
http://hdl.lib.byu.edu/1877/2431
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/