The Gaussian kernel plays a central role in machine learning, uncertainty quantification and scattered data approximation, but has received relatively little attention from numerical analysis standpoint. basic problem of finding an algorithm for efficient integration functions reproduced by kernels not been fully solved. In this article we construct two classes algorithms that use <inline-formu...

Reinforcement learning consists of finding policies that maximize an expected cumulative long-term reward in a Markov decision process with unknown transition probabilities and instantaneous rewards. In this article, we consider the problem such optimal while assuming they are continuous functions belonging to reproducing kernel Hilbert space (RKHS). To learn policy, introduce stochastic policy...

The theory of reproducing kernel Hilbert spaces (RKHSs) has been developed into a powerful tool in mathematics and lots applications many fields, especially machine learning. Fractal provides new technologies for making complicated curves fitting experimental data. Recently, combinations fractal interpolation functions (FIFs) methods curve estimations have attracted the attention researchers. W...

Kernel methods, being supported by a well-developed theory and coming with efficient algorithms, are among the most popular successful machine learning techniques. From mathematical point of view, these methods rest on concept kernels function spaces generated kernels, so–called reproducing kernel Hilbert spaces. Motivated recent developments approaches in context interacting particle systems, ...