Randomized Machine Learning Procedures

A new concept of machine learning based on the computer simulation of entropy-optimal randomized models is proposed. The procedures of randomized machine learning (RML) with “hard” and “soft” randomization are considered; the former imply the exact reproduction of empirical balances while the latter...

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Bibliographic Details
Published in:Automation and remote control 2019-09, Vol.80 (9), p.1653-1670
Main Author: Popkov, Yu. S.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
CAE) and Design
Calculus of Variations and Optimal Control
Optimization
Computer simulation
Computer-Aided Engineering (CAD
Control
Linear programming
Machine learning
Mathematics
Mathematics and Statistics
Mechanical Engineering
Mechatronics
Randomization
Robotics
Systems Theory
Topical Issue
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Summary:A new concept of machine learning based on the computer simulation of entropy-optimal randomized models is proposed. The procedures of randomized machine learning (RML) with “hard” and “soft” randomization are considered; the former imply the exact reproduction of empirical balances while the latter their rough reproduction with an accepted approximation criterion. RML algorithms are formulated as functional entropy-linear programming problems. Applications of RML procedures to text classification and the randomized forecasting of migratory interaction of regional systems are presented.
ISSN:0005-1179
1608-3032
DOI:10.1134/S0005117919090078