An introduction to computational learning theory
An introduction to computational learning theory
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Logical settings for concept-learning
Artificial Intelligence
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
Propositionalization approaches to relational data mining
Relational Data Mining
On the difficulty of approximately maximizing agreements
Journal of Computer and System Sciences
Frequent free tree discovery in graph data
Proceedings of the 2004 ACM symposium on Applied computing
A statistical approach to rule learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Kernels over relational algebra structures
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Support vector inductive logic programming
DS'05 Proceedings of the 8th international conference on Discovery Science
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
Multi-class protein fold recognition using large margin logic based divide and conquer learning
Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics
Max-Margin Weight Learning for Markov Logic Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Learning Large Margin First Order Decision Lists for Multi-Class Classification
DS '09 Proceedings of the 12th International Conference on Discovery Science
Multi-Class protein fold recognition using large margin logic based divide and conquer learning
ACM SIGKDD Explorations Newsletter
ENDER: a statistical framework for boosting decision rules
Data Mining and Knowledge Discovery
Reducing the size of databases for multirelational classification: a subgraph-based approach
Journal of Intelligent Information Systems
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We present a new margin-based approach to first-order rule learning. The approach addresses many of the prominent challenges in first-order rule learning, such as the computational complexity of optimization and capacity control. Optimizing the mean of the margin minus its variance, we obtain an algorithm linear in the number of examples and a handle for capacity control based on error bounds. A useful parameter in the optimization problem tunes how evenly the weights are spread among the rules. Moreover, the search strategy for including new rules can be adjusted flexibly, to perform variants of propositionalization or relational learning. The implementation of the system includes plugins for logical queries, graphs and mathematical terms. In extensive experiments, we found that, at least on the most commonly used toxicological datasets, overfitting is hardly an issue. In another batch of experiments, a comparison with margin-based ILP approaches using kernels turns out to be favorable. Finally, an experiment shows how many features are needed by propositionalization and relational learning approaches to reach a certain predictive performance.