Learning Concept Descriptions from Examples with Errors

Abstract

This paper presents a scheme for learning complex descriptions, such as logic formulas, from examples with errors. The basis for learning is provided by a selection criterion which minimizes a combined measure of discrepancy of a description with training data, and complexity of a description. Learning rules for two types of descriptors are derived: one for finding descriptors with good average discrimination over a set of concepts, second for selecting the best descriptor for a specific concept. Once these descriptors are found, an unknown instance can be identified by a search using the descriptors of the first type for a fast screening of candidate concepts, and the second for the final selection of the closest concept.