Relational Instance-Based Learning
نویسندگان
چکیده
A relational instance-based learning algorithm, called Ribl, is motivated and developed in this paper. We argue that instancebased methods o er solutions to the often unsatisfactory behavior of current inductive logic programming (ILP) approaches in domains with continuous attribute values and in domains with noisy attributes and/or examples. Three research issues that emerge when a propositional instance-based learner is adapted to a rst-order representation are identi ed: (1) construction of cases from the knowledge base, (2) computation of similarity between arbitrarily complex cases, and (3) estimation of the relevance of predicates and attributes. Solutions to these issues are developed. Empirical results indicate that Ribl is able to achieve high classi cation accuracy in a variety of domains. to appear in: Proc. 13th International Conference on Machine Learning, L. Saitta (ed.), Morgan Kaufmann, 1996
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