Artificial Intelligence
On the complexity of some inductive logic programming problems
New Generation Computing - Special issue on inductive logic programming 97
Efficient Theta-Subsumption Based on Graph Algorithms
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
Concept learning by structured examples: an algebraic approach
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
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Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples. It uses first-order logic as a uniform representation for examples and hypothesis. In this paper we propose a method to boost any ILP learning algorithm by first decomposing the set of examples to subsets and applying the learning algorithm to each subset separately, second, merging the hypotheses obtained for the subsets to get a single hypothesis for the complete set of examples, and finally refining this single hypothesis to make it shorter. The proposed technique significantly outperforms existing approaches.