The equivalency between a decision tree for classification and a feedback neural network

被引：0
|
作者：
Li, AJ [1 ]
Luo, SW [1 ]
Lin, YH [1 ]
Yu, HB [1 ]
机构：
[1] Beijing Jiaotong Univ, Beijing 100044, Peoples R China
来源：
2004 7TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS 1-3 | 2004年
关键词：
classification; decision tree; feedback neural network; interpolation representation;
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：
In machine learning, the learning paradigms of an artificial neural network (ANN) and a decision tree (DT) are different. but they are equivalent in essence. This paper proves the approximate equivalency between feedback neural networks and decision trees. The result provides us a very useful guideline Mien we perform theoretical research and applications on DT and ANN.
引用
收藏
页码：1558 / 1561
页数：4
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