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无限隐藏关系模型中的快速推理

Fast Inference in Infinite Hidden Relational Models
课程网址: http://videolectures.net/mlg07_xu_fiiih/  
主讲教师: Zhao Xu
开课单位: 慕尼黑大学
开课时间: 2007-09-05
课程语种: 英语
中文简介:
关系学习是机器学习中越来越受关注的领域(Dzeroski&Lavrac,2001; Friedman等,  1999; Raedt&Kersting,2003)。徐等人。 (2006)引入了无限隐藏关系模型(IHRM)  它在实体关系数据库模型的上下文中查看关系学习与实体,属性和  关系(也比较(Kemp et al。,2006))。在IHRM中,为每个实体引入潜在变量。该  潜变量是其他实体属性的唯一父变量,是关系属性的父变量。该  每个潜在变量中的状态数是特定于实体类的。因此与Dirichlet合作是明智的  过程(DP)混合模型,其中每个实体类可以优化其自身的表示复杂性  自我组织的方式。对于我们的讨论,可以说我们将DP混合模型集成到IHRM中  通过简单地让每个实体类的隐藏状态的数量接近无穷大。因此,一个自然的结果  IHRM是对实体的聚类,提供对域结构的有趣洞察。
课程简介: Relational learning is an area of growing interest in machine learning (Dzeroski & Lavrac, 2001; Friedman et al., 1999; Raedt & Kersting, 2003). Xu et al. (2006) introduced the infinite hidden relational model (IHRM) which views relational learning in context of the entity-relationship database model with entities, attributes and relations (compare also (Kemp et al., 2006)). In the IHRM, for each entity a latent variable is introduced. The latent variable is the only parent of the other entity attributes and is a parent of relationship attributes. The number of states in each latent variable is entity class specific. Therefore it is sensible to work with Dirichlet process (DP) mixture models in which each entity class can optimize its own representational complexity in a self-organized way. For our discussion it is sufficient to say that we integrate a DP mixture model into the IHRM by simply letting the number of hidden states for each entity class approach infinity. Thus, a natural outcome of the IHRM is a clustering of the entities providing interesting insight into the structure of the domain.
关 键 词: 关系学习; 无限隐藏关系模型; 实体关系数据库模型
课程来源: 视频讲座网
最后编审: 2019-06-30:cjy
阅读次数: 34