A Brief Digest on Reproducing Kernel Hilbert Space

Abstract

Reproducing Kernel Hilbert Space (RKHS) is a common used tool in statistics and machine learning to generalize from linear models to non-linear models. In this paper we will try to understand the basic theoretical results in studying RKHS: to construct a RKHS starting from a given kernel function. This view is highly related to the kernel methods for regression and classification in the area of machine learning.