Models of recognition algorithms based on linear threshold functions

Abstract

Abstract The problem of constructing a model of recognition algorithms in the conditions of high dimensionality of feature space is considered. To solve this problem, a new approach suggests that it takes into account the interconnectedness of the given features. The main concern of this approach is to isolate representative traits and form preferred combinations of traits. A distinctive feature of the proposed model of algorithms is to determine a suitable set of elementary threshold rules within the framework of the preferred combinations of representative features and build a proximity function based on this set of feature combinations. Scientifically, the results of this work in the aggregate represent a new solution to a scientific problem related to improving the reliability of recognition algorithms based on radial functions. The practical significance of the results lies in the fact that the developed algorithms can be applied in medical and technical diagnostics, geological forecasting, biometric identification. To test the performance of the proposed model, experimental studies were performed.