Synthesis and analysis of classifiers based on generalized model of identification

Abstract

In this paper we propose a generalized model of identification which displays flexible transformation within the framework of generally known paradigms by changing tunings. The application of this model enables to synthesize various classifiers using a priori information about definite applied tasks of identification. So, we describe the approach to the solution of the problem of generation of representative training sequences and correct comparative evaluation of classifiers.