A method that combines inductive learning with exemplar-based learning

Abstract

A learning approach that combines inductive learning with exemplar-based learning is described. In the method, a concept is represented by two parts: a generalized abstract description and a set of exemplars (exceptions). Generalized descriptions represent the principles of concepts, whereas exemplars represent the exceptional or rare cases. The method is an alternative for solving the problem of small disjuncts and for representing concepts with imprecise and irregular boundaries. The method for combining inductive learning and exemplar-based learning has been implemented in the flexible concept learning system. Experiments showed that the combined method has comparable performance to that of AQ16 and ASSISTANT in three natural domains.<>