Reproducing Kernel Banach Spaces for Machine Learning

被引：0
|
作者：
Zhang, Haizhang [1 ]
Xu, Yuesheng [1 ]
Zhang, Jun [2 ]
机构：
[1] Syracuse Univ, Dept Math, Syracuse, NY 13244 USA
[2] Univ Michigan, Dept Psychol, Ann Arbor, MI 48109 USA
来源：
JOURNAL OF MACHINE LEARNING RESEARCH | 2009年 / 10卷
关键词：
reproducing kernel Banach spaces; reproducing kernels; learning theory; semi-inner-products; representer theorems; CLASSIFICATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We introduce the notion of reproducing kernel Banach spaces (RKBS) and study special semi-inner-product RKBS by making use of semi-inner-products and the duality mapping. Properties of an RKBS and its reproducing kernel are investigated. As applications, we develop in the framework of RKBS standard learning schemes including minimal norm interpolation, regularization network, support vector machines, and kernel principal component analysis. In particular, existence, uniqueness and representer theorems are established.
引用
收藏
页码：2741 / 2775
页数：35
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