Methods of Machine-Aided Training in Small Business: Content and Management
https://doi.org/10.21686/2413-2829-2019-6-83-95
Abstract
The article provides key characteristics of standard methods of machine-aided training used by companies in operative business processes. Within the frames of home orientation to the innovation business development, digital economy and infrastructure for data storage the human factor becomes essential. The use of methods of artificial intellect by employees of small enterprises faces obstacles that imply personnel ignorance concerning strategic functionality of available today algorithms of business processes. Small commercial enterprises encounter the problem that they do not know the key instrumental principles of functioning and use of machine-aided training algorithms. At the same time business processes of the enterprise could be seriously improved through implementing algorithms of machine-aided training. The authors conducted a formal and analytical review of potential means for small business optimization. They described types of algorithms and models of machine-aided training, such as multiple regressive model, logistic regression, etc., as well as instrumental problems of their use by enterprise analysts and developers. Recommendations were prepared aimed at use of these models in order to raise the efficiency of small commercial enterprises.
About the Authors
S. A. Tishchenko
Lomonosov Moscow State University
Russian Federation
Russian Federation
Sergey A. Tishchenko – PhD, Assistant Professor of the Department for Economic Informatics
1 Leninskie gory, Moscow, 119991
M. A. Shakhmuradian
Lomonosov Moscow State University
Russian Federation
Russian Federation
Mikhail A. Shakhmuradian – Post-Graduate Student of the Department for Economics of Innovation
1 Leninskie gory, Moscow, 119991
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Review
For citations:
Tishchenko S.A., Shakhmuradian M.A. Methods of Machine-Aided Training in Small Business: Content and Management. Vestnik of the Plekhanov Russian University of Economics. 2019;(6):83-95. (In Russ.) https://doi.org/10.21686/2413-2829-2019-6-83-95
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