|
Showing 1 - 1 of
1 matches in All Departments
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 unrivalled 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.
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.