Weighted Learning for Feedforward Neural Networks

被引：0

作者：

Rong-Fang Xu ^{[1
]}

Thao-Tsen Chen ^{[1
]}

Shie-Jue Lee ^{[1
]}

机构：

[1] Department of Electrical Engineering, National Sun Yat-Sen University

来源：

Journal of Electronic Science and Technology | 2014年 / 03期

关键词：

Extreme learning machine; hybrid learning; instance-based learning; weighted least squares;

D O I：

暂无

中图分类号：

TP183 [人工神经网络与计算];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose two weighted learning methods for the construction of single hidden layer feedforward neural networks. Both methods incorporate weighted least squares. Our idea is to allow the training instances nearer to the query to offer bigger contributions to the estimated output. By minimizing the weighted mean square error function, optimal networks can be obtained. The results of a number of experiments demonstrate the effectiveness of our proposed methods.

引用

页码：299 / 304

页数：6

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