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统计谓词的发明

Statistical Predicate Invention
课程网址: http://videolectures.net/icml07_kok_spi/  
主讲教师: Stanley Kok
开课单位: 华盛顿大学
开课时间: 2007-07-27
课程语种: 英语
中文简介:
我们提出统计谓词发明是统计关系学习的一个关键问题。SPI是在结构化数据中发现新概念、新属性和新关系的问题,它推广了统计模型中的隐变量发现和ILP中的谓词发明。我们提出了一个基于二阶马尔可夫逻辑的SPI初始模型,其中谓词和参数都可以是变量,语篇的领域事先不完全知道。我们的方法基于出现在原子中的符号的簇,迭代地改进符号的簇(例如,它通过它们相关的对象的簇将关系聚类)。由于不同的聚类可以更好地预测原子的不同子集,我们允许多个横切聚类。在大量的关系数据集上,我们证明了这种方法优于马尔科夫逻辑结构学习和最近引入的无限关系模型。
课程简介: We propose statistical predicate invention as a key problem for statistical relational learning. SPI is the problem of discovering new concepts, properties and relations in structured data, and generalizes hidden variable discovery in statistical models and predicate invention in ILP. We propose an initial model for SPI based on second-order Markov logic, in which predicates as well as arguments can be variables, and the domain of discourse is not fully known in advance. Our approach iteratively refines clusters of symbols based on the clusters of symbols they appear in atoms with (e.g., it clusters relations by the clusters of the ob jects they relate). Since different clusterings are better for predicting different subsets of the atoms, we allow multiple cross-cutting clusterings. We show that this approach outperforms Markov logic structure learning and the recently introduced infinite relational model on a number of relational datasets.
关 键 词: 归纳逻辑编程; 统计模型; 结构学习
课程来源: 视频讲座网
数据采集: 2022-11-08:chenjy
最后编审: 2022-11-08:chenjy
阅读次数: 23