New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Algorithmic Program DeBugging
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Nonmonotonic inductive logic programming by instance patterns
Proceedings of the 9th ACM SIGPLAN international conference on Principles and practice of declarative programming
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Effectiveness and efficiency are two most important properties of ILP approaches. For both top-down and bottom-up search-based approaches, greater efficiency is usually gained at the expense of effectiveness. In this paper, we propose a bottom-up approach, called ILP by instance patterns, for the problem of concept learning in ILP. This approach is based on the observation that each example has its own pieces of description in the background knowledge, and the example together with these descriptions constitute a instance of the concept subject to learn. Our approach first captures the instance structures by patterns, then constructs the final theory purely from the patterns. On the effectiveness aspect, this approach does not assume determinacy of the learned concept. On the efficiency aspect, this approach is more efficient than existing ones due to its constructive nature, the fact that after the patterns are obtained, both the background and examples are not needed anymore, and the fact that it does not perform coverage test and needs no theorem prover.