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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,
面頁冊數:	1 online resource (x, 182 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 05 Apr 2016).
標題:	Kernel functions - Congresses. -
電子資源:	https://doi.org/10.1017/CBO9781316219232
ISBN:	9781316219232 (ebook)

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

An introduction to the theory of reproducing kernel Hilbert spaces /Vern I. Paulsen, Mrinal Raghupathi. - 1 online resource (x, 182 pages) :digital, PDF file(s). - Cambridge studies in advanced mathematics ;152. - Cambridge studies in advanced mathematics ;134..

Title from publisher's bibliographic system (viewed on 05 Apr 2016).

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: 9781316219232 (ebook)Subjects--Topical Terms:

892964
Kernel functions
--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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