NEUROANALITICAL METHOD OF IMAGE CLASSIFICATION
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Abstract
In this article theoretical justification of use of neural networks at recognition of images is considered. As a result of research the neuroanalytical algorithm allowing unmistakably to carry out classification of images in the absence of noise of entrance signals is created. But the algorithm of recognition of images has to be effective and in the presence of noise. Noisy entrance signals are classified by existing neuronetwork algorithms unsatisfactorily. Experiments with insignificant noise in 0,2 pixels confirm only slight increase of accuracy. Check of efficiency of the developed neuroanalytical algorithm led to conclusions that it is necessary to develop and research robustn algorithm of classification which will be steady both to insignificant and against very considerable noise.
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References
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