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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格式 PDF ● 网页 122 ● ISBN 9781470450779 ● 出版者 American Mathematical Society ● 发布时间 2019 ● 下载 3 时 ● 货币 EUR ● ID 8057344 ● 复制保护 Adobe DRM
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