| 概 率 逻 辑 学 习 概 述 |
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| 引用本文： | 陈建中,刘大有,,孙舒杨,,Stephen Muggleton.概 率 逻 辑 学 习 概 述[J].吉林大学学报(理学版),2006,44(6):73-79. |
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| 作者姓名： | 陈建中 刘大有 孙舒杨 Stephen Muggleton |
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| 作者单位： | 1. 英国帝国理工学院 计算机系, 英国 伦敦 南肯辛顿 SW7 2AZ； 2. 吉林大学 计算机科学与技术学院, 长春 130012； 3. 吉林大学 符号计算与知识工程教育部重点实验室, 长春 130012 |
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| 摘 要： | 在归纳逻辑编程研究的基础上, 给出了概率逻辑学习的一种形式化定义框架. 根据不同概率逻辑学习方法在实例表示和概率定义上的不同, 讨论了3种概率逻辑学习框架： 解释学习、 证明学习和蕴涵学习; 并据此对现有的典型概率逻辑学习方法和系统进行了分析和概述.
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| 关 键 词： | 概率逻辑学习 统计关系学习 归纳逻辑编程 学习框架 |
| 收稿时间： | 2006-05-18 |
Survey of Probabilistic Logic Learning |
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| CHEN Jian zhong,LIU Da you,SUN Shu yang,Stephen Muggleton.Survey of Probabilistic Logic Learning[J].Journal of Jilin University: Sci Ed,2006,44(6):73-79. |
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| Authors: | CHEN Jian zhong LIU Da you SUN Shu yang Stephen Muggleton |
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| Institution: | 1. Department of Computing, Imperial College London, South Kensington SW7 2AZ, London, UK; 2. College of Computer Science and Technology, Jilin University, Changchun 130012, China; 3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China |
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| Abstract: | A formal definition of Probabilistic Logic Learning is presented on the basis of Inductive Logic Programming research. Three learning settings for various Probabilistic Logic Learning approaches: probabilistic learning from interpretations, proofs and entailment are addressed based on their representation of examples and probabilistic processes. The paperinvestigates a survey of some existing Probabilistic Logic Learning approaches and systems with the three settings. |
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| Keywords: | probabilistic logic learning statistical relational learning inductive logic programming learning settings |
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