A Message Entropy Method of Learning from Examples

被引：0

作者：

刘占生

武新华

夏松波

机构：

来源：

Journal of Harbin Institute of Technology | 1997年 / 03期

关键词：

Machine learning; decision tree; extension matrix; message entropy; fault diagnosis;

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：

Extension matrix（EM）and ID3 are two main algorithms with wide applicationin the field of machine learning,but analysis shows EM may Cause false diagnosis and ID3may cause fail diagnosis.This paper presents a new combination of these two methods toachieve a new method called entropy extension matrix（EEM）and a new concept ofgeneralization association ability（GAA）.Results show that this algorithm has propertiesbetter than those of EM and ID3.

引用

页码：10 / 13

页数：4

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