Scientific and Technical Journal


ISSN Print 2221-3937
ISSN Online 2221-3805
In automated systems of visual information processing - in industry, in medicine, in security systems method of classification largely determines the accuracy of decisions. Education in the classification of such systems is often carried out by small sets of parameters for the complex shape of the clusters and their intersection in the feature space. When classifying into operation when it is necessary to recognize objects invariant to transformations of scale, rotate, shear in images with noise and distorted form, it can lead to reduced reliability. Increased reliability by increasing the data set increases resource use and increases the time for learning these systems in the case of introduction of new products in the industry, improvements in methods of medical drugs, etc. When training and debugging system in such cases it is necessary to select the parameters of the classifier - from a range of values of coefficients that determine the shape of the surfaces separating the classes in the feature space. The authors have developed a method of classification using multi-start optimization with wavelet transform. In this paper, we propose a method of classification, which can help to determine a set of sub-ranges of factors separating surfaces to allow for the selection of the classifier reliability required from the perspective of pragmatic adequacy, based on the known properties of the wavelet transform to carry out spatial processing with adjustable detail.

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