A New Method for Constructing Radial Basis Function Neural Networks
Jinyan Sun1, Xizhao Wang
1Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University
Available Online October 2007.
- 10.2991/iske.2007.182How to use a DOI?
- Radial basis function neural network, Norm, Training error, Sensitivity
Ignoring the samples far away from the training samples, our study team gives a new norm-based derivative process of localized generalization error boundary. Enlightened by the above research, this paper proposes a new method to construct radial basis function neural networks, which minimizes the sum of training error and stochastic sensitivity. Experimental results show that the new method can lead to simple and better network architecture
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jinyan Sun AU - Xizhao Wang PY - 2007/10 DA - 2007/10 TI - A New Method for Constructing Radial Basis Function Neural Networks BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 1071 EP - 1076 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.182 DO - 10.2991/iske.2007.182 ID - Sun2007/10 ER -