Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Experimental comparison of human and machine learning formalisms
Proceedings of the sixth international workshop on Machine learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
Inductive logic programming for relational knowledge discovery
New Generation Computing - Special issue on inductive logic programming 97
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP
Data Mining and Knowledge Discovery
A Tight Integration of Pruning and Learning (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
A New Design and Implementation of Progol by Bottom-Up Computation
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Relational Knowledge Discovery in Databases
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Realizing Progol by Forward Reasoning
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Searching the Subsumption Lattice by a Genetic Algorithm
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
A Note on Two Simple Transformations for Improving the Efficiency of an ILP System
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Concurrent Execution of Optimal Hypothesis Search for Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Which Hypotheses Can Be Found with Inverse Entailment?
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
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Since learning with Inductive Logic Programming (ILP) can be regarded as the search problem through the hypotheses space, it is essential to reduce the search space in order to improve the efficiency. In the propositional learning framework, an efficient admissible search algorithm called OPUS (Optimized Pruning for Unordered Search) has been developed. OPUS employed the effective pruning techniques for unordered search and succeeded in improving the efficiency. In this paper, we propose an application of OPUS to an ILP system Progol. However, because of the difference of representation language, it is not applicable to ILP directly. We make the conditions clear under which the pruning techniques in OPUS can be applied in the framework of Progol. In addition, we propose a new pruning criterion, which can be regarded as inclusive pruning. Experiments are conducted to assess the effectiveness of the proposed algorithms. The results show that the proposed algorithms reduce the number of candidate hypotheses to be evaluated as well as the computational time for a certain class of problems.