Recommended Citation
Published in IEEE International Joint Conference on Neural Networks, June 1, 2008, pages 3313-3318. Copyright © 2008 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. The definitive version is available online at: http://dx.doi.org/10.1109/IJCNN.2008.4634268.
Abstract
The choice of network dimension is a fundamental issue in neural network applications. An optimal neural network topology not only reduces the computational complexity, but also improves its generalization capacity. In this research, a new pruning algorithm based on cross validation and sensitivity analysis is developed and compared with three existing pruning algorithms on various pattern classification problems. Computer simulation results show the network size can be significantly reduced using this new algorithm while the neural network still maintains satisfactory generalization accuracy.
Disciplines
Electrical and Computer Engineering
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