Entropy Randomization in Machine Learning

Author:   Yuri S. Popkov ,  Alexey Yu. Popkov ,  Yuri A. Dubnov
Publisher:   Taylor & Francis Ltd
ISBN:  

9781032307749


Pages:   392
Publication Date:   08 October 2024
Format:   Paperback
Availability:   In Print   Availability explained
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Entropy Randomization in Machine Learning


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Author:   Yuri S. Popkov ,  Alexey Yu. Popkov ,  Yuri A. Dubnov
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.725kg
ISBN:  

9781032307749


ISBN 10:   1032307749
Pages:   392
Publication Date:   08 October 2024
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Yuri S. Popkov: Doctor of Engineering, Professor, Academician of Russian Academy of Sciences; Chief Researcher at Federal Research Center “Computer Science and Control,” Russian Academy of Sciences; Chief Researcher at Trapeznikov Institute of Control Sciences, Russian Academy of Sciences; Professor at Lomonosov Moscow State University. Author of more than 250 scientific publications, including 15 monographs. His research interests include stochastic dynamic systems, optimization, machine learning, and macrosystem modeling. Alexey Yu. Popkov: Candidate of Sciences, Leading Researcher at Federal Research Center “Computer Science and Control,” Russian Academy of Sciences; author of 47 scientific publications. His research interests include software engineering, high-performance computing, data mining, machine learning, and entropy methods. Yuri A. Dubnov: MSc in Physics, Researcher at Federal Research Center “Computer Science and Control,” Russian Academy of Sciences. Author of more than 18 scientific publications. His research interests include machine learning, forecasting, randomized approaches, and Bayesian estimation.

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