O'Reilly logo

Machine Learning by Sergios Theodoridis

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Chapter 11

Learning in Reproducing Kernel Hilbert Spaces

Abstract

This chapter is dedicated to nonparametric modeling of nonlinear functions in reproducing kernel Hilbert spaces (RKHS). The basic definitions and concepts behind RKH spaces are presented, including positive definite kernels, reproducing kernels, kernel matrices, and the kernel trick. Cover’s theorem and the representer theorem are introduced. Then, kernel ridge regression, support vector regression, and support vector machines are studied. Online algorithms for learning in RKH spaces, such as the kernel LMS, NORMA, and the kernel APSM are discussed. The notion of multiple kernel learning is presented and a discussion on sparse modeling for nonparametric models in the context ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required