Hidden Common Cause Relations in Relational Learning
Hidden Common Cause Relations in Relational Learning
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发表时间：
2007-12
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通讯作者：
Ricardo Silva;Wei Chu;Zoubin Ghahramani
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作者：
Ricardo Silva;Wei Chu;Zoubin Ghahramani
在为关系数据库中的对象预测类标签时，考虑关系模型通常是有帮助的：这允许类标签之间的信息共享并提高预测性能。但是，可以通过不同的方式在关系数据库中关联对象。一种传统的方式对应于马尔可夫网络结构：每个现有的关系由一条无向边表示。这意味着，在输入要素的条件下，每个对象标签都独立于给定其在图中的邻居的其他对象标签。然而，没有理由马尔可夫网络应该是对称依赖结构的唯一选择。在这里，我们讨论由于隐藏的共同原因而假定关系存在的情况。我们讨论了生成的图形模型与马尔可夫网络的不同之处，以及它如何描述现实世界中不同类型的关系过程。在此图形表示的基础上建立了贝叶斯非参数分类模型，并通过几项实证研究进行了评估。
When predicting class labels for objects within a relational database, it is often helpful to consider a model for relationships: this allows for information between class labels to be shared and to improve prediction performance. However, there are different ways by which objects can be related within a relational database. One traditional way corresponds to a Markov network structure: each existing relation is represented by an undirected edge. This encodes that, conditioned on input features, each object label is independent of other object labels given its neighbors in the graph. However, there is no reason why Markov networks should be the only representation of choice for symmetric dependence structures. Here we discuss the case when relationships are postulated to exist due to hidden common causes. We discuss how the resulting graphical model differs from Markov networks, and how it describes different types of real-world relational processes. A Bayesian nonparametric classification model is built upon this graphical representation and evaluated with several empirical studies.