E3S Web Conf.
Volume 224, 2020Topical Problems of Agriculture, Civil and Environmental Engineering (TPACEE 2020)
|Number of page(s)||8|
|Section||Mathematical Models for Environmental Monitoring and Assessment|
|Published online||23 December 2020|
Comparison of the efficiency of neural network algorithms in recognition and classification problems
Don state technical University, 1, Gagarin square, 344000, Rostov-on-Don, Russia
* Corresponding author: firstname.lastname@example.org
The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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