MAXIMIZING EXPECTED GENERALIZATION FOR LEARNING COMPLEX QUERY CONCEPTS

摘要

A method of learning a user query concept is provided which includes a sample selection stage and a feature reduction stage; during the sample selection stage, sample objects are selected from a query concept sample space bounded by a k-ĐNF and a k-DNF; the selected sample objects include feature sets that are no more than a prescribed amount different from a corresponding feature set defined by the k-CNF; during the feature reduction stage, individual features are removed from the k-CNF that are identified as differing from corresponding individual features of sample objects indicated by the user to be close to the user's query concept; also during the feature reduction stage, individual features are removed from the k-DNF that are identified as not differing from corresponding individual features of sample objects indicated by the user to be not close to the user's query concept.