This paper foucses on a major step of machine learning, namely checking whether an example matches a candidate hypothesis. In relational learning, matching can be viewed as a Constraint Satisfaction Problem (CSP). The complexity of the task is analyzed in the Phase Transition framework, investigating the impact on the effectiveness of two relational learners: FOIL and G-NET.
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