The concept learning in the theory of rough sets

被引：0
|
作者：
Zhang, Qun-Feng [1 ]
Jiang, Yu-Ting [2 ]
Li, Zhi-Qiang [3 ]
机构：
[1] Hebei Univ, Coll Math & Comp Sci, Key Lab Machine Learning & Computat Intelligence, Baoding 071002, Peoples R China
[2] Hebei Univ, Ind & Commercial Coll, Baoding 071002, Peoples R China
[3] Hebei Informat Engn Sch, Baoding 071000, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年
关键词：
rough set; concept learning; sample complexity; PAC-learnability;
D O I：
10.1109/ICMLC.2008.4620427
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.
引用
收藏
页码：337 / +
页数：2
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