Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension
Machine Learning - Special issue on computational learning theory
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Machine Learning
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Relational learning as search in a critical region
The Journal of Machine Learning Research
A genetic algorithms approach to ILP
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Five problems in five areas for five years
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
QG/GA: A Stochastic Search for Progol
Inductive Logic Programming
ProGolem: a system based on relative minimal generalisation
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
A comparative study on ILP-based concept discovery systems
Expert Systems with Applications: An International Journal
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Learning theories using estimation distribution algorithms and (reduced) bottom clauses
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Fitness function based on binding and recall rate for genetic inductive logic programming
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
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Most search techniques within ILP require the evaluation of a large number of inconsistent clauses. However, acceptable clauses typically need to be consistent, and are only found at the "fringe" of the search space. A search approach is presented, based on a novel algorithm called QG (Quick Generalization). QG carries out a random-restart stochastic bottom-up search which efficiently generates a consistent clause on the fringe of the refinement graph search without needing to explore the graph in detail. We use a Genetic Algorithm (GA) to evolve and re-combine clauses generated by QG. In this QG/GA setting, QG is used to seed a population of clauses processed by the GA. Experiments with QG/GA indicate that this approach can be more efficient than standard refinement-graph searches, while generating similar or better solutions.