Geodesic
Effective retraining "models" in the method of multivariate interpolation
Matematičeskoe modelirovanie, Tome 28 (2016) no. 4, pp. 92-98 Cet article a éte moissonné depuis la source Math-Net.Ru

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We investigate machine learning method based on the theory of random functions. This paper shows a method of rapid retraining "model" when adding new data to the existing ones. This approach reduces the computational complexity of constructing an updated "model" from $O(m^3)$ to from $O(m^2)$. The term "model" means interpolating or approximating function constructed from the training data. This approach can have an independent value in the area of linear algebra as applied to a well-conditioned linear systems with symmetric matrix.
Keywords: machine learning, random function, system of linear equations.
Mots-clés : interpolation 
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     author = {U. N. Bakhvalov and I. V. Kopylov},
     title = {Effective retraining "models" in the method of multivariate interpolation},
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     url = {http://geodesic.mathdoc.fr/item/MM_2016_28_4_a6/}
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U. N. Bakhvalov; I. V. Kopylov. Effective retraining "models" in the method of multivariate interpolation. Matematičeskoe modelirovanie, Tome 28 (2016) no. 4, pp. 92-98. http://geodesic.mathdoc.fr/item/MM_2016_28_4_a6/

[1] Iu. N. Bakhvalov, Metod mnogomernoi interpoliacii i approksimacii i ego prilozhenia, Sputnik+, M., 2007, 108 pp.

[2] Iu. N. Bakhvalov, L. L. Malygin, P. S. Cherkas, “Metod mashinnogo obycheniia na osnove mnogomernoi interpoliacii i approksimacii sluchainykh funktsii”, Vestnik Cherepovetskogo gosudarstvennogo universiteta, 2:2 (2012), 7–9