Noise-resistant incremental relational learning using possible worlds

Authors:
James Westendorp
Affiliations:
The University of New South Wales, Sydney, Australia
Venue:
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Year:
2002

Citing 6
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Abstract

Incremental learning from noisy data is a difficult task and has received very little attention in the field of Inductive Logic Programming. This paper outlines an approach to noisy incremental learning based on a possible worlds model and its implementation in NILE. Several issues relating to the use of this model are addressed. Empirical results are shown for an existing batch domain and also for an interactive learning task.