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Lookup NU author(s): Dr Jie ZhangORCiD
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A training method for enhancing neural network model generalisation is proposed in this paper. In this method, a neural network is trained and tested alternatively on a training data set and a testing data set. Unlike in conventional neural network training where the training and testing data sets are fixed, the training and testing data sets swap roles continuously during network training. Training is terminated when the network prediction errors on both data sets cannot be further reduced. Application examples demonstrate that this neural network training strategy can significantly improve neural network model prediction accuracy, especially long range prediction accuracy.
Author(s): Zhang J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Joint Conference on Neural Networks
Year of Conference: 2002
Pages: 800-805
Publisher: IEEE
URL: http://dx.doi.org/10.1109/IJCNN.2002.1005576
DOI: 10.1109/IJCNN.2002.1005576
Library holdings: Search Newcastle University Library for this item
ISBN: 0780372786
