A function fitting method

• Autorzy: Dachiraju Rajesh

Identyfikatory

DOI

https://doi.org/10.1515/jaa-2020-2004

• Języki publikacji: EN

Abstrakty

EN

In this article, we describe a function fitting method that has potential applications in machine learning and also prove relevant theorems. The described function fitting method is a convex minimization problem which can be solved using a gradient descent algorithm. We also provide qualitative analysis on fitness to data of this function fitting method. The function fitting problem is also shown to be a solution of a linear, weak partial differential equation (PDE). We describe a simple numerical solution using a gradient descent algorithm that converges uniformly to the actual solution. As the functional of the minimization problem is a quadratic form, there also exists a numerical method using linear algebra.

Słowa kluczowe

EN

function fitting; Sobolev functions; machine learning

PL

dopasowanie funkcji; funkcje Sobolewa; uczenie maszynowe

• Wydawca: De Gruyter
• Czasopismo: Journal of Applied Analysis
• Rocznik: 2020
• Tom: Vol. 26, nr 1
• Strony: 59--65
• Opis fizyczny: Bibliogr. 13 poz.

Twórcy

autor

Dachiraju Rajesh

• Independent Researcher, F-306 BRC Shivahills, Puppalaguda Main Road, Hyderabad-500089, India

Bibliografia

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• [8] C. Fefferman and B. Klartag, Fitting a Cm-smooth function to data. II, Rev. Mat. Iberoam. 25 (2009), no. 1, 49-273.
• [9] L. A. Gajek, Estimating a density and its derivatives via the minimum distance method, Probab. Theory Related Fields 80 (1989), no. 4, 601-617.
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).