Machine Learning - special issue on inductive logic programming
Logical settings for concept-learning
Artificial Intelligence
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
Learning Horn Expressions with LOGAN-H
The Journal of Machine Learning Research
ILP, the blind, and the elephant: Euclidean embedding of co-proven queries
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
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A novel inductive logic programming system, called Classic'cl is presented. Classic'cl integrates several settings for learning, in particular learning from interpretations and learning from satisfiability. Within these settings, it addresses descriptive and probabilistic modeling tasks. As such, Classic'cl (C-armr, cLAudien, icl-S(S)at, ICl, and CLlpad) integrates several well-known inductive logic programming systems such as Claudien, Warmr (and its extension C-armr), ICL, ICL-SAT, and LLPAD. We report on the implementation, the integration issues as well as on some experiments that compare Classic'cl with some of its predecessors.