Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2005.1555984
Title: Effective neural network pruning using cross-validation
Authors: Huynh, T.Q.
Setiono, R. 
Issue Date: 2005
Source: Huynh, T.Q.,Setiono, R. (2005). Effective neural network pruning using cross-validation. Proceedings of the International Joint Conference on Neural Networks 2 : 972-977. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2005.1555984
Abstract: This paper addresses the problem of finding neural networks with optimal topology such that their generalization capability is maximized. Our approach is to combine the use of a penalty function during network training and a subset of the training samples for cross-validation. The penalty is added to the error function so that the weights of network connections that are not useful have small magnitude. Such network connections can be pruned if the resulting accuracy of the network does not change beyond a preset level. Training samples in the cross-validation set are used to indicate when network pruning is terminated. Our results on 32 publicly available data sets show that the proposed method outperforms existing neural network and decision tree methods for classification. © 2005 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/42824
ISBN: 0780390482
DOI: 10.1109/IJCNN.2005.1555984
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