COMBINING THE RESULTS OF SEVERAL NEURAL-NETWORK CLASSIFIERS

被引：251
|
作者：
ROGOVA, G [1 ]
机构：
[1] CALSPAN CORP,BUFFALO,NY 14221
来源：
NEURAL NETWORKS | 1994年 / 7卷 / 05期
关键词：
CLASSIFIER; NEURAL NETWORK; CHARACTER RECOGNITION; THE DEMPSTER-SHAFER THEORY OF EVIDENCE; EVIDENCE;
D O I：
10.1016/0893-6080(94)90099-X
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Neural networks and traditional classifiers work well for optical character recognition; however, it is advantageous to combine the results of several algorithms to improve classification accuracies. This paper presents a combination method based on the Dempster-Shafer theory of evidence, which uses statistical information about the relative classification strengths of several classifiers. Numerous experiments show the effectiveness of this approach. Our method allows 15-30% reduction of misclassification error compared to the best individual classifier
引用
收藏
页码：777 / 781
页数：5
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