Finding accurate frontiers: a knowledge-intensive approach to relational learning

Authors:
Michael Pazzani;Clifford Brunk
Affiliations:
Department of Information and Computer Science, University of California, Irvine, CA;Department of Information and Computer Science, University of California, Irvine, CA
Venue:
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Year:
1993

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Abstract

An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory.