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Information Processing Letters
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Horn approximations of empirical data
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Communications of the ACM
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Top-down induction of first-order logical decision trees
Artificial Intelligence
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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
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Information and Computation
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Machine Learning
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Constraint-based Learning of Long Relational Concepts
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning Horn Expressions with LogAn-H
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Learning Acyclic First-Order Horn Sentences from Entailment
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
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ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A theoretical comparison of selected csp solving and modeling techniques
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The Journal of Machine Learning Research
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The Journal of Machine Learning Research
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
CLASSIC'CL: an integrated ILP system
DS'05 Proceedings of the 8th international conference on Discovery Science
Fast estimation of first-order clause coverage through randomization and maximum likelihood
Proceedings of the 25th international conference on Machine learning
Towards clausal discovery for stream mining
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Learning first-order definite theories via object-based queries
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
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The paper introduces LOGAN-H a system for learning first-order function-free Horn expressions from interpretations. The system is based on an algorithm that learns by asking questions and that was proved correct in previous work. The current paper shows how the algorithm can be implemented in a practical system, and introduces a new algorithm based on it that avoids interaction and learns from examples only. The LOGAN-H system implements these algorithms and adds several facilities and optimizations that allow efficient applications in a wide range of problems. As one of the important ingredients, the system includes several fast procedures for solving the subsumption problem, an NP-complete problem that needs to be solved many times during the learning process. We describe qualitative and quantitative experiments in several domains. The experiments demonstrate that the system can deal with varied problems, large amounts of data, and that it achieves good classification accuracy.