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Generalized Mercer Kernels and Reproducing Kernel Banach Spaces (Memoirs of the American Mathematical Society)

Mathematical Analysis

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About the Author Yuesheng Xu, Syracuse University, NY.Qi Ye, South China Normal University, Guangzhou, China. Product Description 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 the authors 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, they develop a new concept of generalized Mercer kernels to construct $p$-norm RKBSs for $1\leq p\leq\infty$.

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Format: Paperback
ASIN: 1470435500
Category: Books > Subjects > Science, Nature & Maths > Mathematics > Mathematical Analysis
Domain: Amazon UK
Release Date: 28 February 2019
Listed Since: 21 December 2018

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Generalized Mercer Kernels and Reproducing Kernel Banach Spaces (Memoirs of the American Mathematical Society)

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