Approximating reproducing kernel Hilbert space functions by Bernstein operators
DOI10.1007/S00025-024-02253-WzbMATH Open1548.68188MaRDI QIDQ6617623
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Publication date: 11 October 2024
Published in: (Search for Journal in Brave)
approximation theorykernel methodsmachine learningreproducing kernel Hilbert spaceBernstein operators
Learning and adaptive systems in artificial intelligence (68T05) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22) Approximation by operators (in particular, by integral operators) (41A35)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Sur l'approximation de fonctions intégrables sur l'interval-0,1-ferme par des polynômes de Bernstein modifies
- Local smoothness of functions and Bernstein-Durrmeyer operators
- Optimal approximation of piecewise smooth functions using deep ReLU neural networks
- Error bounds for approximations with deep ReLU networks
- Better numerical approximation by Durrmeyer type operators
- Approximation with polynomial kernels and SVM classifiers
- Learning theory estimates via integral operators and their approximations
- Learning Theory
- Approximating functions with multi-features by deep convolutional neural networks
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