Building a model of recognizing operators based on the definition of basic reference objects

Abstract

Abstract The paper considers issues related to the construction of a model of recognizing operators, focused on the classification of objects in conditions of high dimensionality of a feature space. A new approach to constructing a model of recognition operators based on the construction of multi-level proximity functions is proposed. In this case, the construction of the model was carried out within the framework of recognition algorithms based on the calculation of estimates. The goal of this work is develop a model is to form independent subsets of interrelated objects, highlighting a set of representative pairs of features. A distinctive feature of the proposed operator model is the definition of a suitable set of threshold functions when constructing an extremal recognition operator. The purpose of this article is to develop a model of recognizing operators based on the calculation of estimates using two-dimensional threshold rules. Scientifically, the results of this work in aggregate represent a new solution of a scientific problem related to the issues of increasing the reliability of recognizing operators based on the evaluation of estimates. The practical significance of the results lies in the fact that the developed operators and programs can be applied in medical and technical diagnostics, geological forecasting, biometric identification and other areas where it is possible to solve the problem of classifying objects defined in a space of large dimensionality.