Improving generalization ability of self-generating neural networks through ensemble averaging

被引：0
|
作者：
Inoue, H [1 ]
Narihisa, H [1 ]
机构：
[1] Okayama Univ Sci, Dept Informat & Comp Engn, Okayama 7000005, Japan
来源：
KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS: CURRENT ISSUES AND NEW APPLICATIONS | 2000年 / 1805卷
关键词：
self-generating neural networks; self-generating neural tree; ensemble averaging; classification; competitive learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present an ensemble averaging effect for improving the generalization capability of self-generating neural networks applied to classification problems. The results of our computational experiments show that ensemble averaging effect is 1-7% improvements in accuracy comparing with single SGNN for three benchmark problems.
引用
收藏
页码：177 / 180
页数：4
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