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An introduction to the theory of rep...

Raghupathi, Mrinal.

An introduction to the theory of reproducing kernel Hilbert spaces

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	An introduction to the theory of reproducing kernel Hilbert spaces/ Vern I. Paulsen, Mrinal Raghupathi.
作者:	Paulsen, Vern I.,
其他作者:	Raghupathi, Mrinal.
出版者:	Cambridge :Cambridge University Press, : 2016.,
面頁冊數:	x, 182 p. :ill., digital ; : 24 cm.;
標題:	Hilbert space - Congresses. -
電子資源:	https://doi.org/10.1017/CBO9781316219232
ISBN:	9781316219232

An introduction to the theory of reproducing kernel Hilbert spaces
Paulsen, Vern I.,1951-

An introduction to the theory of reproducing kernel Hilbert spaces[electronic resource] /Vern I. Paulsen, Mrinal Raghupathi. - Cambridge :Cambridge University Press,2016. - x, 182 p. :ill., digital ;24 cm. - Cambridge studies in advanced mathematics ;152. - Cambridge studies in advanced mathematics ;134..

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.

ISBN: 9781316219232Subjects--Topical Terms:

896695
Hilbert space
--Congresses.

LC Class. No.: QA322.4 / .P38 2016

Dewey Class. No.: 515.733

An introduction to the theory of reproducing kernel Hilbert spaces
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520 $a 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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