architecture selection, cross validation, artificial neural network
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.
Physical and Mathematical Sciences
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