BUILDING OF RECOGNITION OPERATORS IN CONDITION OF FEATURES’ CORRELATIONS

Authors

  • Sh. Kh. Fazilov Tashkent University of information technologies, Tashkent, Uzbekistan, Uzbekistan
  • N. M. Mirzaev Tashkent University of information technologies, Tashkent, Uzbekistan, Uzbekistan
  • O. N. Mirzaev Tashkent University of information technologies, Tashkent, Uzbekistan, Uzbekistan

DOI:

https://doi.org/10.15588/1607-3274-2016-1-7

Keywords:

pattern recognition, model of recognition operators, potential function, features’ correlations, subset of strongly correlated features, representative feature, preferred correlation model.

Abstract

The problem of recognizing of patterns given in the space of correlated features is considered. The new approach to the building of model
of recognition operators, which considers the correlation of given features, is proposed. The building of the model is carried out for potential
function type recognition operators. The main idea of the proposed approach is formation of uncorrelated subsets of strongly correlated
features and extracting preferred correlation model for each of subsets of strongly correlated features. Analysis of the results shows that the
considered recognition operators are used in cases when there is a certain correlation between objects belonging to the same class. When the
expression of this relationship is weak, classical model of recognition operators is used. The main advantage of the proposed recognition
operators is to improve the accuracy and the significant reduction in the volume of computational operations in recognition of unknown
objects, which allows them to use when building recognition systems working in real time.

References

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Published

2015-12-02

How to Cite

Fazilov, S. K., Mirzaev, N. M., & Mirzaev, O. N. (2015). BUILDING OF RECOGNITION OPERATORS IN CONDITION OF FEATURES’ CORRELATIONS. Radio Electronics, Computer Science, Control, (1). https://doi.org/10.15588/1607-3274-2016-1-7

Issue

No. 1 (2016): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

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Copyright (c) 2016 Sh. Kh. Fazilov, N. M. Mirzaev, O. N. Mirzaev

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