Mathematical Methods of Randomized Machine Learning

被引：0
|
作者：
Popkov Y.S. [1 ]
机构：
[1] Institute for Systems Analysis of the Federal Research Center “Informatics and Management” of the Russian Academy of Sciences, Moscow
来源：
Journal of Mathematical Sciences | 2021年 / 254卷 / 5期
关键词：
68T05; 97R40; machine learning; mathematical methods;
D O I：
10.1007/s10958-021-05331-4
中图分类号：
学科分类号：
摘要：
In this paper, a review of mathematical methods of randomized machine learning is presented. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.
引用
收藏
页码：652 / 676
页数：24
相关论文
共 50 条
  • [31] COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case
    Pavlyutin, Matvey
    Samoyavcheva, Marina
    Kochkarov, Rasul
    Pleshakova, Ekaterina
    Korchagin, Sergey
    Gataullin, Timur
    Nikitin, Petr
    Hidirova, Mohiniso
    MATHEMATICS, 2022, 10 (02)
  • [32] Reinforcement Procedure for Randomized Machine Learning
    Popkov, Yuri S.
    Dubnov, Yuri A.
    Popkov, Alexey Yu.
    MATHEMATICS, 2023, 11 (17)
  • [33] MATHEMATICAL FOUNDATIONS AND COMPUTATIONAL METHODS FOR A DIGITAL LOGIC MACHINE
    LEDLEY, RS
    JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF AMERICA, 1954, 2 (03): : 249 - 274
  • [34] A novel randomized machine learning approach: Reservoir computing extreme learning machine
    Ertugrul, Omer Faruk
    APPLIED SOFT COMPUTING, 2020, 94
  • [35] Using Machine Learning to Analyze the Learning Process for Solving Mathematical Problems
    Bauyrzhan, Nauryzbayev
    Sakysh, Baygamitova
    Zhanar, Akhmetova
    Nikolay, Pak
    Ardak, Karipzhanova
    Kumys, Urazbaeva
    International Journal of Interactive Mobile Technologies, 2022, 16 (21): : 114 - 124
  • [36] Machine learning methods in organ transplantation
    Guijo-Rubio, David
    Antonio Gutierrez, Pedro
    Hervas-Martinez, Cesar
    CURRENT OPINION IN ORGAN TRANSPLANTATION, 2020, 25 (04) : 399 - 405
  • [37] Machine learning methods for predictive proteomics
    Barla, Annalisa
    Jurman, Giuseppe
    Riccadonna, Samantha
    Merler, Stefano
    Chierici, Marco
    Furlanello, Cesare
    BRIEFINGS IN BIOINFORMATICS, 2008, 9 (02) : 119 - 128
  • [38] Geochemistry and machine learning: methods and benchmarking
    Prasianakis, N. I.
    Laloy, E.
    Jacques, D.
    Meeussen, J. C. L.
    Miron, G. D.
    Kulik, D. A.
    Idiart, A.
    Demirer, E.
    Coene, E.
    Cochepin, B.
    Leconte, M.
    Savino, M. E.
    Samper-Pilar, J.
    De Lucia, M.
    Churakov, S. V.
    Kolditz, O.
    Yang, C.
    Samper, J.
    Claret, F.
    ENVIRONMENTAL EARTH SCIENCES, 2025, 84 (05)
  • [39] Kernel methods in Quantum Machine Learning
    Riccardo Mengoni
    Alessandra Di Pierro
    Quantum Machine Intelligence, 2019, 1 : 65 - 71
  • [40] Machine learning methods for microbiome studies
    Namkung, Junghyun
    JOURNAL OF MICROBIOLOGY, 2020, 58 (03) : 206 - 216
12345