图 书 馆
学习概率逻辑程序的参数Learning the Parameters of Probabilistic Logic Programs from Interpretations |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_thon_problog/ |
| 主讲教师: | Ingo Thon; KU Leuven |
| 开课单位: | 鲁汶大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  ProbLog是逻辑编程语言Prolog的最新引入的概率扩展，其中事实可以用事实的可能性进行注释。这种概率语言的优势在于，与使用声明性模型的解释相比，它自然表达了一个生成过程。解释是关系描述或可能的世界。本文介绍了一种新颖的参数估计算法LFI ProbLog，用于从部分解释中学习ProbLog程序。该算法本质上是软EM算法。它为每种解释构造了一个命题逻辑公式，用于估计概率参数的边际。对LFI ProbLog算法进行了实验验证，结果证明了该方法的正确性并证明了该方法的有效性。 |
| 课程简介: | ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, in which facts can be annotated with the probability that they hold. The advantage of this probabilistic language is that it naturally expresses a generative process over interpretations using a declarative model. Interpretations are relational descriptions or possible worlds. This paper introduces a novel parameter estimation algorithm LFI-ProbLog for learning ProbLog programs from partial interpretations. The algorithm is essentially a Soft-EM algorithm. It constructs a propositional logic formula for each interpretation that is used to estimate the marginals of the probabilistic parameters. |
| 关 键 词: | ProbLog; 概率逻辑; 逻辑语言; LFI ProbLog; 参数估算法; EM算法; 命题逻辑公式 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-04-02：zhouxj |
| 最后编审: | 2020-05-31：王勇彬(课程编辑志愿者) |
| 阅读次数: | 92 |