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基于粗集理论知识表达系统的一种归纳学习方法
引用本文:吴福保,李奇.基于粗集理论知识表达系统的一种归纳学习方法[J].控制与决策,1999,14(3):206-211.
作者姓名:吴福保  李奇
作者单位:东南大学自动化研究所
摘    要:基于粗集(RS)理论,针对知识表达系统提出一种新的归纳学习方法,对该方法中条件属性的简化,核值表的求取,决策规则的约简进行了详细讨论,并给出相应的求解算法,本方法为机器学习以及从数据库中进行机器发现提供了新的思路。

关 键 词:知识表达系统  归纳学习  粗集理论  机器学习

Inductive Learning Approach to Knowledge Representation System Based on Rough Set Theory
Wu Fubao,Li Qi,Song Wenzhong.Inductive Learning Approach to Knowledge Representation System Based on Rough Set Theory[J].Control and Decision,1999,14(3):206-211.
Authors:Wu Fubao  Li Qi  Song Wenzhong
Affiliation:Southeast University
Abstract:The paper proposed a new inductive learning approach to Knowledge Representation System based on Rough Set Theory. In the paper, we discuss on the reduction of conditional attributes, the acquisition of core table and the reduction of decision rules and then give a computing algorithm. The approach presents a new idea to machine learning and knowledge discovery from databases.
Keywords:rough set theory  knowledge representation system  decision table  inductive learning  decision rule  
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