A New Method for Constructing Radial Basis Function Neural Networks

摘要：

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.

关键词： Radial basis function neural network,Norm, Training error Sensitivity

作者: Jinyan Sun Xizhao Wang

作者单位: Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University, Baoding 071002, P.R. China

会议类型: 国际会议

会议名称: The 2007 International Conference on Intelligent Systems and Knowledge Engineering(第二届智能系统与知识工程国际会议)

会议地点: 成都

会议语种:英文

页码: 1240-1245

在线出版日期: 2007-10-15（万方平台首次上网日期，不代表论文的发表时间）