General multivariate arctangent function activated neural network approximations
Journal of Numerical Analysis and Approximation Theory, 2022 Here we expose multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or \(\mathbb{R}^{N}\), \(N\in \mathbb{N}\), by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature ...
George A. Anastassiou
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Hyperbolic Tangent Like Relied Banach Space Valued Neural Network Multivariate Approximations
Annals of the West University of Timisoara: Mathematics and Computer Science, 2023 Here we examine the multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝN , N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network ...
Anastassiou George A.
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Multiple general sigmoids based Banach space valued neural network multivariate approximation
Cubo, 2023 Here we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or \(\mathbb{R}^{N},\) \(N\in \mathbb{N}\), by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature ...
George A. Anastassiou
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Trigonometric Induced Multivariate Smooth Gauss–Weierstrass Singular Integrals Approximation
Mathematics, 2023 In this article, we employ the uniform and Lp, 1 ...
George A. Anastassiou
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Neural Network Approximation for Time Splitting Random Functions
Mathematics, 2023 In this article we present the multivariate approximation of time splitting random functions defined on a box or RN,N∈N, by neural network operators of quasi-interpolation type.
George A. Anastassiou +1 more
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The rate of convergence of bounded linear processes on spaces of continuous functions
Journal of Numerical Analysis and Approximation Theory, 2023 Quantitative Korovkin-type theorems for approximation by bounded linear operators defined on \(C(X,d)\) are given, where \((X,d)\) is a compact metric space. Special emphasis is on positive linear operators.
Heiner Gonska
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Approximations by multivariate sublinear and Max-product operators under convexity
Demonstratio Mathematica, 2018 Here we search quantitatively under convexity the approximation of multivariate function by general multivariate positive sublinear operators with applications to multivariate Max-product operators.
Anastassiou George A.
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A new generalization of the Takagi function [PDF]
, 2015 We consider a one-parameter family of functions $\{F(t,x)\}_{t}$ on $[0,1]$ and partial derivatives $\partial_{t}^{k} F(t, x)$ with respect to the parameter $t$. Each function of the class is defined by a certain pair of two square matrices of order two.
Okamura, Kazuki
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On adaptive estimation of linear functionals [PDF]
, 2005 Adaptive estimation of linear functionals over a collection of parameter spaces is considered. A between-class modulus of continuity, a geometric quantity, is shown to be instrumental in characterizing the degree of adaptability over two parameter spaces
Cai, T. Tony, Low, Mark G.
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Optimal inference in a class of regression models [PDF]
, 2016 We consider the problem of constructing confidence intervals (CIs) for a linear functional of a regression function, such as its value at a point, the regression discontinuity parameter, or a regression coefficient in a linear or partly linear regression.
Armstrong, Timothy B., Kolesár, Michal
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