文献类型：期刊文献

英文题名：The improved learning rate for regularized regression with RKBSs

作者：Liu, Huanxiang[1];Sheng, Baohuai[2];Ye, Peixin[3]

机构：[1]Zhejiang Gongshang Univ, Sch Math & Stat, Hangzhou 310018, Zhejiang, Peoples R China;[2]Shaoxing Univ, Dept Math, Shaoxing 312000, Peoples R China;[3]Nankai Univ, Sch Math, Tianjin 300071, Peoples R China

年份：2017

卷号：8

期号：4

起止页码：1235

外文期刊名：INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS

收录：SCI-EXPANDED(收录号：WOS:000405297300014)、、EI(收录号：20172803928663)、Scopus(收录号：2-s2.0-85022080844)、WOS

语种：英文

外文关键词：Reproducing kernel Banach space; Regularized regression; Convex analysis

外文摘要：The investigation on the performance of learning from samples of functions in Banach spaces is a new research field. A key theoretical problem we need to investigate is how the convergence rate is influenced by the geometry property of the Banach spaces. In the present paper, we provide the learning rate for the kernel regularized regression based on reproducing kernel Banach spaces. The rate is provided in both expected mean and empirical mean. The results show that the uniform convexity influences the learning rate.