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Distribution-free bounds for relational classification

机译：关系分类的无分布界

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摘要

Statistical relational learning (SRL) is a subarea in machine learning which addresses the problem of performing statistical inference on data that is correlated and not independently and identically distributed (i.i.d.) - as is generally assumed. For the traditional i.i.d. setting, distribution-free bounds exist, such as the Hoeffding bound, which are used to provide confidence bounds on the generalization error of a classification algorithm given its hold-out error on a sample size of N. Bounds of this form are currently not present for the type of interactions that are considered in the data by relational classification algorithms. In this paper, we extend the Hoeffding bounds to the relational setting. In particular, we derive distribution-free bounds for certain classes of data generation models that do not produce i.i.d. data and are based on the type of interactions that are considered by relational classification algorithms that have been developed in SRL. We conduct empirical studies on synthetic and real data which show that these data generation models are indeed realistic and the derived bounds are tight enough for practical use.
机译：统计关系学习（SRL）是机器学习中的一个子领域，它解决了对关联的数据进行统计推断的问题，这些数据并非独立且相同地分布（i.d.对于传统的i.d.在设置中，存在无分布的边界，例如霍夫丁边界，这些边界用于提供分类算法的泛化误差的置信边界，因为其对N的样本量具有保持误差。当前不存在这种形式的边界关系分类算法在数据中考虑的交互类型。在本文中，我们将Hoeffding边界扩展到关系设置。特别是，我们为某些类别的不生成i.d的数据生成模型导出了无分布边界。数据，并基于在SRL中开发的关系分类算法考虑的交互类型。我们对合成数据和真实数据进行了实证研究，这些研究表明这些数据生成模型确实是现实的，并且派生的边界对于实际使用而言足够紧。

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