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A Deep Learning Method Study of User Interest Classification
P. 154-159.
Malafeev A., Nikolaev K.
In this paper, a deep learning method study is conducted to solve a new multiclass text classification problem, identifying user interests by text messages. We used an original dataset of almost 90 thousand forum text messages, labeled for ten interests. We experimented with different modern neural network architectures: recurrent and convolutional, as well as simpler feedforward networks. Classification accuracy was evaluated for different architectures, text representations, and sets of miscellaneous parameters.
Keywords: машинное обучениенейронные сетиneural networksmachine learningклассификация текстовdeep learningconvolutional neural networkstext classificationсверточные нейронные сетиглубокое обучениеuser interestsfeedforward neural networkslong short term memoryинтересы пользователейнейронные сети с прямой связьюдолгосрочная кратковременная память
Publication based on the results of:
Alexandrov D., Программная инженерия 2022 Vol. 13 No. 7 P. 331-343
Trends in computer vision and pattern recognition and capabilities of modern computers contributed to a consid- erable amount of research of these areas application in facial recognition systems. The purpose of this paper is to investigate the most significant methods of face recognition. In the first two sections of current paper, the methods of face ...
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A. Maevskiy, F. Ratnikov, Zinchenko A. et al., The European Physical Journal C - Particles and Fields 2021 Vol. 81 Article 599
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In the field of cryptoeconomics the Ethereum (Ethereum Foundation) project gave opportunity to create “own” cryptocurrency – new token based on its smart-contract platform to everyone without lowlevel programming skills. Then it became obvious that tokens could be used for crowdfunding as the Ethereum did in 2014. Unregulated and easy to access such scheme became popular among related to ...
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Perov A., Пестунов А. И., Прикладная дискретная математика 2020 № 3 С. 46-56
The paper explores possibility of applying convolutional neural networks to the security analysis of iterative block ciphers. A new approach for constructing distinguishing
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Beknazarov N., Jin S., Poptsova M., Scientific Reports 2020 Vol. 10 P. 19134
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Romanyuk K., , in : 2018 Fifth HCT Information Technology Trends (ITT). : IEEE, 2018. P. 1-6.
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Zykov S. V., Андрианова Е. Г., Жуков Д. О. et al., Российский технологический журнал 2018 Т. 7 № 1 С. 4-45
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Nikolaev K., Malafeev A., , in : Analysis of Images, Social Networks and Texts. 7th International Conference AIST 2018. : Springer, 2018. Ch. 12. P. 121-126.
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Кусакин И. К., Федорец О. В., Romanov A., Научно-техническая информация. Серия 2: Информационные процессы и системы 2022 Т. 12 С. 6-9
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Piscataway : IEEE, 2020
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Krylov V., Krylov S., Journal of Physics: Conference Series 2018 Т. 1117 № conference 1
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