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Fast Estimation of Relational Pattern Coverage through Randomization and Maximum Likelihood
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. In inductive logic programming, theta-subsumption is a widely used coverage test. Unfortunately, testing theta-subsumption is NP-complete, which represents a crucial efficiency bottleneck for many relational learners. In this paper, we present a probabilistic estimator of clause coverage, based on a randomized restarted search strategy. Under a distribution assumption, our algorithm can estimate clause coverage without having to decide subsumption for all examples. We implement this algorithm in program ReCovEr. On generated graph data and real-world datasets, we show that ReCovEr provides reasonably accurate estimates while achieving dramatic runtimes improvements compared to a state-of-the-art algorithm
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
