Tree clustering for constraint networks (research note)
Artificial Intelligence
Decomposing constraint satisfaction problems using database techniques
Artificial Intelligence
Characterizing the applicability of classification algorithms using meta-level learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
On the efficiency of subsumption algorithms
Journal of the ACM (JACM)
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Logical settings for concept-learning
Artificial Intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
A comparison of structural CSP decomposition methods
Artificial Intelligence
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Phase Transitions in Relational Learning
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Ideal Theory Refinement under Object Identity
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Theta-Subsumption Based on Graph Algorithms
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Logic and Learning
A theoretical comparison of selected csp solving and modeling techniques
A theoretical comparison of selected csp solving and modeling techniques
Relational learning as search in a critical region
The Journal of Machine Learning Research
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
1BC2: a true first-order Bayesian classifier
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning Horn Expressions with LOGAN-H
The Journal of Machine Learning Research
Fast estimation of first-order clause coverage through randomization and maximum likelihood
Proceedings of the 25th international conference on Machine learning
θ-Subsumption Based on Object Context
Inductive Logic Programming
Arc Consistency Projection: A New Generalization Relation for Graphs
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
A Restarted Strategy for Efficient Subsumption Testing
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Taming the Complexity of Inductive Logic Programming
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Semantic matchmaking of web services constraint conditions
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Constraint models for reasoning on unification in inductive logic programming
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
ProGolem: a system based on relative minimal generalisation
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
When does it pay off to use sophisticated entailment engines in ILP?
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Guiding the search in the NO region of the phase transition problem with a partial subsumption test
ECML'06 Proceedings of the 17th European conference on Machine Learning
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
On generating templates for hypothesis in inductive logic programming
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Efficient operations in feature terms using constraint programming
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
A Restarted Strategy for Efficient Subsumption Testing
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
An empirical study of encodings for group MaxSAT
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Decomposition, merging, and refinement approach to boost inductive logic programming algorithms
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Reducing examples in relational learning with bounded-treewidth hypotheses
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Relational learning and Inductive Logic Programming (ILP) commonly use as covering test the 驴-subsumption test defined by Plotkin. Based on a reformulation of 驴-subsumption as a binary constraint satisfaction problem, this paper describes a novel 驴-subsumption algorithm named Django,1 which combines well-known CSP procedures and 驴-subsumption-specific data structures. Django is validated using the stochastic complexity framework developed in CSPs, and imported in ILP by Giordana et Saitta. Principled and extensive experiments within this framework show that Django improves on earlier 驴-subsumption algorithms by several orders of magnitude, and that different procedures are better at different regions of the stochastic complexity landscape. These experiments allow for building a control layer over Django, termed Meta-Django, which determines the best procedures to use depending on the order parameters of the 驴-subsumption problem instance. The performance gains and good scalability of Django and Meta-Django are finally demonstrated on a real-world ILP task (emulating the search for frequent clauses in the mutagenesis domain) though the smaller size of the problems results in smaller gain factors (ranging from 2.5 to 30).