Approximation by multivariate max-product Kantorovich-type operators and learning rates of least-squares regularized regression

DOI10.3934/cpaa.2020189zbMath1472.41011OpenAlexW3027390819MaRDI QIDQ2191850

Gianluca Vinti, Danilo Costarelli, Lucian C. Coroianu, Sorin Gheorghe Gal

Publication date: 26 June 2020

Published in: Communications on Pure and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/cpaa.2020189

zbMATH Keywords

learning theoryK-functionalsampling Kantorovich operatorsBorel probability measuressample errorregularizing functionmultivariate generalized kernelsmultivariate max-productregularized error

Mathematics Subject Classification ID

Multidimensional problems (41A63) Rate of convergence, degree of approximation (41A25) Approximation by operators (in particular, by integral operators) (41A35)

Related Items (8)

Some new theorems on the approximation of maximum product type of multivariate nonlinear Bernstein-Chlodowsky operators ⋮ Regular summability methods in the approximation by max-min operators ⋮ Approximation by multivariate max-product Kantorovich exponential sampling operators ⋮ Complex Shepard operators and their summability ⋮ Convergence of sampling Kantorovich operators in modular spaces with applications ⋮ Approximation by mixed operators of max-product-Choquet type ⋮ Approximation by max-product operators of Kantorovich type ⋮ Connections between the Approximation Orders of Positive Linear Operators and Their Max-Product Counterparts

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