Publication year: 2022
Internet Resource: Please Login to download book
Addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: Includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. Considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
Subject: Artificial Intelligence, Statistical Learning, Computational Intelligence, Data Science, Machine Learning, Kernel, Bayesian Statistics, Hilbert Space, reproducing kernel Hilbert space, RKHS, Python