Generalized Mercer kernels and reproducing kernel Banach spaces
著者
書誌事項
Generalized Mercer kernels and reproducing kernel Banach spaces
（Memoirs of the American Mathematical Society, no.1243）
American Mathematical Society, 2019
大学図書館所蔵 件 / 全8件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
"March 2019, volume 258, number 1243 (seventh of 7 numbers)"
内容説明・目次
内容説明
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 $1\leq p\leq\infty$.
目次
Introduction
Reproducing Kernel Banach Spaces
Generalized Mercer Kernels
Positive Definite Kernels
Support Vector Machines
Concluding Remarks
Acknowledgments
Index
Bibliography.
「Nielsen BookData」 より