An Introduction to the Theory of Reproducing Kernel Hilbert Spaces

Nonfiction, Science & Nature, Mathematics, Functional Analysis, Mathematical Analysis
Cover of the book An Introduction to the Theory of Reproducing Kernel Hilbert Spaces by Vern I. Paulsen, Mrinal Raghupathi, Cambridge University Press
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Author: Vern I. Paulsen, Mrinal Raghupathi ISBN: 9781316557563
Publisher: Cambridge University Press Publication: April 11, 2016
Imprint: Cambridge University Press Language: English
Author: Vern I. Paulsen, Mrinal Raghupathi
ISBN: 9781316557563
Publisher: Cambridge University Press
Publication: April 11, 2016
Imprint: Cambridge University Press
Language: English

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.

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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.

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