Logical correctors in the problem of classification by precedents

Abstract

The problem of recognition (classification) by precedents is considered. Issues of improving the recognition ability and the training rate of logical correctors, i.e., the recognition procedures based on the construction of correct sets of elementary classifiers, are studied. The concept of a correct set of generic elementary classifiers is introduced and used to construct and investigate a qualitatively new model of the logical corrector. This model uses a wider class of correcting functions than in the earlier constructed models of logical correctors.