Gradient learning algorithm for ensembling neural network

被引：0

作者：

Meng, Jiang ^{[1
]}

An, Kun ^{[2
]}

机构：

[1] North Univ China, Sch Mech Engn & Automatizat, Taiyuan 030051, Peoples R China

[2] North Univ China, Sch Informat & Commun Engn, Taiyuan 030051, Peoples R China

来源：

2006 9TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION, VOLS 1- 5 | 2006年

关键词：

neural network ensemble; gradient descent; generalization performance; GASEN;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Aimed at similarity between neural network ensemble and simple neural network, a novel gradient learning algorithm for ensemble (GLAENN) is presented based on the gradient descent method. The new algorithm can improve the generalization error by modifying subnet weights after the ensemble subnets are trained individually. The simulation results indicate GLAENN is of similar function to GASEN but with a different idea; further, it is of better generalization performance than GASEN, bagging and single network.

引用

页码：1658 / +

页数：2

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