首页 | 本学科首页   官方微博 | 高级检索  
     

基于粗糙集理论的示例学习研究
引用本文:杭小树,熊范纶. 基于粗糙集理论的示例学习研究[J]. 浙江大学学报(理学版), 2002, 29(3): 346-354
作者姓名:杭小树  熊范纶
作者单位:1. 中国科学技术大学,自动化系,安徽,合肥,230026中国科学院合肥智能机械研究所,安徽,合肥,230031
2. 中国科学院合肥智能机械研究所,安徽,合肥,230031
基金项目:国家自然科学基金重点资助项目 (6 9835 0 0 1)
摘    要:到目前为止,一些启发式算法被提出用于基于扩张矩阵理论的示例学习研究,该文基于粗集理论研究示例学习问题,提出了粗集理论下的几个新概念,如:必要选择子,核选择子集,约简选择子集和所产生复合的评价指标;精确度、覆盖度和简单性,给出了扩张矩阵的粗糙集算法,并提出了基于覆盖度和简单性的遗传算法最优示例学习方法。

关 键 词:粗糙集 示例学习 扩张矩阵 遗传算法
文章编号:1008-9497(2002)03-0346-09
修稿时间:2001-04-20

Study on learning from examples based on rough sets theory
HANG Xiao-shu ,,XIONG Fan-lun. Study on learning from examples based on rough sets theory[J]. Journal of Zhejiang University(Sciences Edition), 2002, 29(3): 346-354
Authors:HANG Xiao-shu     XIONG Fan-lun
Affiliation:HANG Xiao-shu 1,2,XIONG Fan-lun 2
Abstract:Up to now, some heuristic algorithms have been proposed for learning from examples based on extension matrix theory. The approach of learning from examples in this paper is based on rough set theory. Several new concepts are proposed in this paper, such as indispensable selector, core set of selectors, reducted set of selectors as well as accuracy, coverage and simplicity for evaluating a complex. The algorithms for resolving extension matrix and a GA method of optimal learning from examples are also represented.
Keywords:rough sets  learning from examples  extension matrix  generic algorithm
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《浙江大学学报(理学版)》浏览原始摘要信息
点击此处可从《浙江大学学报(理学版)》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号