J4

• 计算机科学 • 上一篇    下一篇

RSILP模型若干问题的研究

孙成敏, 刘大有, 孙舒杨   

  1. 吉林大学 计算机科学与技术学院, 长春 130012; 吉林大学 符号计算与知识工程教育部重点实验室, 长春 130012
  • 收稿日期:2006-06-15 修回日期:1900-01-01 出版日期:2007-03-26 发布日期:2007-03-26
  • 通讯作者: 孙成敏

Some Questions on RSILP Models

SUN Chengmin, LIU Dayou, SUN Shuyang   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory ofSymbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
  • Received:2006-06-15 Revised:1900-01-01 Online:2007-03-26 Published:2007-03-26
  • Contact: SUN Chengmin

摘要: 利用基于粗糙集理论的统计关系学习方法, 解决了统计关系学习中归纳逻辑程序设计方法的不确定性问题, 并改进了粗糙集结合归纳逻辑程序设计的系列模 型, 从而得到更加准确的模型体系定义. 改进后的模型系列更合理完善.

关键词: 统计关系学习, 粗糙集, 归纳逻辑程序设计, RSILP模型, VPRSILP模型

Abstract: The uncertainty and vagueness, which can not be dealt with independently via the Inductive Logic Programming (ILP) in Statistical Relat ional Learning, was resolved by means of the Statistical Relational Learning (SRL) method based on Rough Set (RS) theory. And the models combined with RS and ILP were perfected and improved. Such work, which further perfects these models and makes them more rational.

Key words: statistical relational learning, rough set, inductive logic programming, RSILP model, VPRSILP model

中图分类号: 

  • TP18