An Introduction to the Theory of Reproducing Kernel Hilbert Spaces

Reproducing kernel Hilbert spaces have developed into an important tool in many areas,
especially statistics and machine learning, and they play a valuable role in complex
analysis, probability, group representation theory, and the theory of integral operators.
This unique text offers a unified overview of the topic, providing detailed examples of
applications, as well as covering the fundamental underlying theory, including chapters
on interpolation and approximation, Cholesky and Schur operations on kernels, and
vector-valued spaces. Self-contained and accessibly written, with exercises at the end
of each chapter, this unrivaled treatment of the topic serves as an ideal introduction for
graduate students across mathematics, computer science, and engineering, as well as a
useful reference for researchers working in functional analysis or its applications.