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

页码：177 / 180

页数：4

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