A Brief Digest on Reproducing Kernel Hilbert Space

Shou-yu TONG, Fu-zhong CONG, Zhi-xia WANG

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.

Keywords


Kernel, Reproducing Kernel Hilbert Space


DOI
10.12783/dtcse/cmee2016/5339

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