Generalized Mercer Kernels and Reproducing Kernel Banach Spaces

This article studies constructions of reproducing kernel Banach spaces (RKBSs) which may be viewed as a generalization of reproducing kernel Hilbert spaces (RKHSs). A key point is to endow Banach spaces with reproducing kernels such that machine learning in RKBSs can be well-posed and of easy implem...

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Hlavní autoři: Xu, Yuesheng, Ye, Qi
Médium: E-kniha Kniha
Jazyk:angličtina
Vydáno: Providence, Rhode Island American Mathematical Society 2019
Vydání:1
Edice:Memoirs of the American Mathematical Society
Témata:
Banach spaces
Functions of complex variables
Geometric function theory
Kernel functions
Support vector machines
Generalized Mercer Kernels
Machine Learning
Support Vector Machines
Sparse Learning Methods
Positive Definite Kernels
Reproducing Kernel Banach Spaces
ISBN:9781470435509, 1470435500
ISSN:0065-9266, 1947-6221
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Shrnutí:This article studies constructions of reproducing kernel Banach spaces (RKBSs) which may be viewed as a generalization of reproducing kernel Hilbert spaces (RKHSs). A key point is to endow Banach spaces with reproducing kernels such that machine learning in RKBSs can be well-posed and of easy implementation. First we verify many advanced properties of the general RKBSs such as density, continuity, separability, implicit representation, imbedding, compactness, representer theorem for learning methods, oracle inequality, and universal approximation. Then, we develop a new concept of generalized Mercer kernels to construct
Bibliografie:March 2019, volume 258, number 1243 (seventh of 7 numbers)
Includes bibliographical references and index
ISBN:9781470435509
1470435500
ISSN:0065-9266
1947-6221
DOI:10.1090/memo/1243