Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Relational rule induction with CPROGO14.4: a tutorial introductuon
Relational Data Mining
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
Scaling Boosting by Margin-Based Inclusionof Features and Relations
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Extension of the Top-Down Data-Driven Strategy to ILP
Inductive Logic Programming
Learning Minesweeper with multirelational learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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A popular way of dealing with the complexity of learning from examples is to proceed in an example-driven fashion. In the past, several researchers have shown that using an example-driven approach, it is possible to learn even structurally complex generalizations which would have been difficult to find using other multirelational learning (ILP) algorithms. On the other hand, it is also well known that the quality of the learning results in example-driven learning may depend on the ordering of the examples; however, such stability issues have received almost no attention. In this paper, we present empirical results in several multirelational application domains to show that instability actually affects the performance of a well-known example-driven ILP system. At the same time, we examine one possible solution to the instability problem, presenting an algorithm which relies on stochastically selected examples and parallel search. We show that our algorithm almost eliminates the instability of example-driven search with limited additional effort.