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Systems and means of deep learning for classification problems
O. Yu. Bakhteeva, M. S. Popovaa, V. V. Strijovb a Moscow Institute of Physics and Technology, 9 Institutskiy Per., Dolgoprudny, Moscow Region 141700, Russian Federation
b A. A. Dorodnicyn Computing Center, Federal Research Center
"Computer Science and Control" of the Russian Academy of Sciences, 40 Vavilov Str., Moscow 119333, Russian Federation
Abstract:
The paper provides a guidance on deep learning net construction and optimization using graphics processing unit. The paper proposes to use GPU-instances on the cloud platform Amazon Web Services. The problem of time series classification is considered. The paper proposes to use a deep learning net, i. e., a multilevel superposition of models, belonging to the following classes: restricted Boltzman machines, autoencoders, and neural nets with softmax-function in output. The proposed method was tested on a dataset containing time segments from mobile phone accelerometer. The analysis of relation between classification error, dataset size, and superposition parameter amount has been conducted.
Keywords:
time series classification; deep learning; model superposition; autoencoder; restricted Boltzmann machine; cloud service.
Received: 14.12.2015
Citation:
O. Yu. Bakhteev, M. S. Popova, V. V. Strijov, “Systems and means of deep learning for classification problems”, Sistemy i Sredstva Inform., 26:2 (2016), 4–22
Linking options:
https://www.mathnet.ru/eng/ssi459 https://www.mathnet.ru/eng/ssi/v26/i2/p4
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Abstract page: | 339 | Full-text PDF : | 121 | References: | 63 |
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