Self-organizing symbolic learned rules

被引：0

作者：

Bahamonde, A ^{[1
]}

de la Cal, EA ^{[1
]}

Ranilla, J ^{[1
]}

Alonso, J ^{[1
]}

机构：

[1] Univ Oviedo, Ctr Inteligencia Artificial, E-33271 Gijon, Spain

来源：

BIOLOGICAL AND ARTIFICIAL COMPUTATION: FROM NEUROSCIENCE TO TECHNOLOGY | 1997年 / 1240卷

关键词：

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 this paper we present a self-organizing process for rules obtained from a machine learning system. The resulting map can be interpreted back into the symbolic field in an attempt to make the logical representation of the original rubs reflect the relationships codified by map distances. Thus, we improve the quality of the starting set of rules both in classification accuracy and in conceptual clarity.

引用

页码：536 / 545

页数：10

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