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

In a recent paper, for univariate max-product sampling operators based on general kernels with bounded generalized absolute moments, we have obtained several Lpμconvergence properties on bounded intervals or on the whole real axis. In this paper, firstly we obtain quantitative estimates with respect to a K-functional, for the multivariate Kantorovich variant of these max-product sampling operators with the integrals written in terms of Borel probability measures. Applications of these approximation results to learning theory are obtained.