Fast neural network ensemble learning via negative-correlation data correction

被引：14

作者：

Chan, ZSH ^{[1
]}

Kasabov, N ^{[1
]}

机构：

[1] Auckland Univ Technol, KEDRI, Auckland, New Zealand

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 06期

关键词：

distributed computing; ensemble learning; negative correlation (NC) learning;

D O I：

10.1109/TNN.2005.852859

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This letter proposes a new negative correlation (NC) learning method that is both easy to implement and has the advantages that: 1) it requires much lesser communication overhead than the standard NC method and 2) it is applicable to ensembles of heterogenous networks.

引用

页码：1707 / 1710

页数：4

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