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On some properties of randomized machine learning procedures in the presence of noisy data
- Authors: Popkov Y.S.1
-
Affiliations:
- Federal Research Center “Computer Science and Control” of Russian Academy of Sciences
- Issue: No 2 (2023)
- Pages: 89-95
- Section: Mathematical modeling
- URL: https://ogarev-online.ru/2071-8632/article/view/286539
- DOI: https://doi.org/10.14357/20718632230209
- ID: 286539
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Abstract
We study various models of measuring noises in the procedures of randomized entropy estimation of probability density functions: additive and multiplicative, measuring noises at the input and output of the object’s model. The properties of entropy-optimal probability density functions are studied, it is shown that the measurement noises corresponding to them are heteroscedastic.
About the authors
Y. S. Popkov
Federal Research Center “Computer Science and Control” of Russian Academy of Sciences
Author for correspondence.
Email: popkov@isa.ru
Federal Reearch Center “Computer Science and Control” of Russian Academy of Sciences, chief research scientist; Member of RAS, Doctor of Science in Engineering, professor; Institute of Control Sciences of Russian Academy of Sciences, chief research scientist. Scientific area: entropy methods, macrosystems, randomized machine learning
Russian Federation, MoscowReferences
- Popkov Yu.S., Popkov A.Yu., Dubnov Yu.A. Entropy Randomization in Mashine Learning, 2023, CRC Press, Taylor & Francis Group.
- Shannon C.E. Mathematical Theory of Communication. 1948, The Bell System Technical Journal, v.27, p.373- 423, 623-656.
- Jaynes E.T. Information theory and statistical Mechanics. Physical Review, 1957, v.104(4), p.620-630.
- Jaynes E.T. Gibbs vs Boltzmann entropy. American Journal of Physics, 1965, v.33, p.391-398.
- Rosenkrantz R.D., Jaynes E.T. Paper on Probability, Statistics,and Statistical Physics. Kluwer Academic Pablishers, 1989.
- Popkov Yu.S. Qualitative Properties of Random Maximum Entropy Estimates of Probability Density Functions. Mathematics, 2021, 9, 548, doi.org/10.3390/math9050548.
- Bollerslev T., Engle R.F., Nelson D.B. ARCH Models. In: Engle R.F. and McFadden D.C.,eds. Handbook of Econometrics, 1994, Elsevier Science, Amsterdam, p.2961-3038.
- Cai T.T., Wang L. Adaptive variance function estimation in heteroscedastic nonparametric regression. Ann. Stat., 2008, v. 36(5), p. 2025-2054, doi: 10.1214/07-AOS509.
- Oreshko N.I. Vosstanovlenie zakona heteroskedaticheskogo shuma pri traektornikh izmereniah na osnove veivlet-tehnologiy // Izvestia SPbLETU “LETI”, 2013, No. 9, pp. 16-21.
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