Please use this identifier to cite or link to this item:
Title: Analysis of Neural Networks for Image Classification
Authors: Vershkov, N. A.
Вершков, Н. А.
Babenko, M. G.
Бабенко, М. Г.
Kuchukov, V. A.
Кучуков, В. А.
Kuchukova, N. N.
Кучукова, Н. Н.
Kucherov, N. N.
Кучеров, Н. Н.
Keywords: Artificial neural networks;Wavelets;Convolution;Correlation;Feature vector;Orthogonal transformations
Issue Date: 2023
Citation: Vershkov, N., Babenko, M., Kuchukov, V., Kuchukova, N., Kucherov, N. Analysis of Neural Networks for Image Classification // Lecture Notes in Networks and Systems. - 2023. - 702 LNNS, pp. 258-269. - DOI: 10.1007/978-3-031-34127-4_25
Series/Report no.: Lecture Notes in Networks and Systems
Abstract: The article explores the option of using information theory’s mathematical tools to model artificial neural networks. The two primary network architectures for image recognition, classification, and clustering are the feedforward network and convolutional networks. The study investigates the use of orthogonal transformations to enhance the effectiveness of neural networks and wavelet transforms in convolutional networks. The research proposes practical applications based on the theoretical findings.
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

Files in This Item:
File SizeFormat 
scopusresults 2700 .pdf
  Restricted Access
132.64 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.