Description
Book SynopsisIn an attempt to introduce application scientists and graduate students to the exciting topic of positive definite kernels and radial basis functions, this book presents modern theoretical results on kernel-based approximation methods and demonstrates their implementation in various settings. The authors explore the historical context of this fascinating topic and explain recent advances as strategies to address long-standing problems. Examples are drawn from fields as diverse as function approximation, spatial statistics, boundary value problems, machine learning, surrogate modeling and finance. Researchers from those and other fields can recreate the results within using the documented MATLAB code, also available through the online library. This combination of a strong theoretical foundation and accessible experimentation empowers readers to use positive definite kernels on their own problems of interest.
Table of ContentsPositive Definite Kernels and Radial Basis Functions; Reproducing Kernel Hilbert Spaces; Kriging; Green's Kernels; Generalized Sobolev Spaces; Alternate and Stable Interpolation Bases; Kernel Optimization; Examples in: Scattered Data Fitting, Surrogate Modeling, Spatial Statistics, Machine Learning, Boundary Value Problems, Finance;
