NEURAL NETWORK ENSEMBLES

被引：2721
|
作者：
HANSEN, LK [1 ]
SALAMON, P [1 ]
机构：
[1] SAN DIEGO STATE UNIV,DEPT MATH SCI,SAN DIEGO,CA 92182
来源：
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1990年 / 12卷 / 10期
关键词：
Crossvalidation; fault tolerant computing; N-version programming; neural networks;
D O I：
10.1109/34.58871
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We propose several means for improving the performance and training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining residual “generalization” error can be reduced by invoking ensembles of similar networks. © 1990 IEEE
引用
收藏
页码：993 / 1001
页数：9
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