Abstract
Motivated by kernel methods in machine learning theory, we study the uniform approximation of functions from reproducing kernel Hilbert spaces by Bernstein operators. Rates of approximation are provided in terms of the function norm in the reproducing kernel Hilbert space. A case study of contracting properties of the Bernstein operators is also presented.
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