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Clustering and instance based learning in first order logic

机译:一阶逻辑中的聚类和基于实例的学习

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摘要

Instance based learning and clustering are popular methods in propositional machine learning. Both methods use a notion of similarity between objects. This dissertation investigates these methods in a relational setting. First, a number of new metrics are proposed. Next, these metrics are used to upgrade clustering and instance based learning to first order logic.
机译:基于实例的学习和聚类是命题机器学习中流行的方法。两种方法都使用对象之间的相似性概念。本文在关系背景下研究了这些方法。首先,提出了许多新指标。接下来,这些指标用于将群集和基于实例的学习升级为一阶逻辑。

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