Generalized Mercer Kernels and Reproducing Kernel Banach Spaces

Download or read book Generalized Mercer Kernels and Reproducing Kernel Banach Spaces written by Yuesheng Xu and published by American Mathematical Soc.. This book was released on 2019-04-10 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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≤p≤∞ .