Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
ECML '93 Proceedings of the European Conference on Machine Learning
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Theoretical Views of Boosting and Applications
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Scaling Boosting by Margin-Based Inclusionof Features and Relations
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Proof Length as an Uncertainty Factor in ILP
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble
ECML '07 Proceedings of the 18th European conference on Machine Learning
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
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In propositional learning, boosting has been a very popular technique for increasing the accuracy of classification learners. In first-order learning, on the other hand, surprisingly little attention has been paid to boosting, perhaps due to the fact that simple forms of boosting lead to loss of comprehensibility and are too slow when used with standard ILP learners. In this paper, we show how both concerns can be addressed by using a recently proposed technique of constrained confidence-rated boosting and a fast weak ILP learner. We give a detailed description of our algorithm and show on two standard benchmark problems that indeed such a weak learner can be boosted to perform comparably to state-of-the-art ILP systems while maintaining acceptable comprehensibility and obtaining short run-times.