An Introduction to the Theory of Reproducing Kernel Hilbert Spaces (Cambridge Studies in Advanced Mathematics, Series Number 152)

Paulsen, Vern I.; Raghupathi, Mrinal

ISBN 10: 1107104092 / ISBN 13: 9781107104099

Published by Cambridge University Press, 2016

About this Item

Seller Inventory # I-9781107104099

Bibliographic Details

• Title: An Introduction to the Theory of Reproducing Kernel Hilbert Spaces (Cambridge Studies in Advanced Mathematics, Series Number 152)
• Publisher: Cambridge University Press
• Publication Date: 2016
• Language: English
• ISBN 10: 1107104092
• ISBN 13: 9781107104099
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Authors

Vern I. Paulsen held a John and Rebecca Moores Chair in the Department of Mathematics, University of Houston, from 1996 to 2015. He is currently a Professor in the Department of Pure Mathematics at the Institute for Quantum Computing, University of Waterloo. He is the author of four books, over 100 research articles, and the winner of several teaching awards.

Mrinal Raghupathi is a Lead Quantitative Risk Analyst at the United Services Automobile Association (USAA). His research involves applications of reproducing kernel Hilbert spaces, risk analysis, and model validation.