An investigation of noise-tolerant relational concept learning
algorithms

Clifford A. Brunk and  Michael J. Pazzani
Department of Information and Computer Science
University of California, Irvine
Irvine, CA 92717 USA
brunk@ics.uci.edu

Abstract

We discuss the types of noise that may occur in relational learning
systems and describe two approaches to addressing noise in a
relational concept learning algorithm.  We then evaluate each approach
experimentally.