文献类型：期刊文献

英文题名：Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSs

作者：Liu, Lin[1];Pan, Xiaoling[1];Sheng, Baohuai[1,2]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Peoples R China;[2]Zhejiang Yuexiu Univ, Dept Econ Stat, Shaoxing 312000, Peoples R China

年份：2023

卷号：2023

外文期刊名：JOURNAL OF MATHEMATICS

收录：SCI-EXPANDED(收录号：WOS:000952118000001)、、Scopus(收录号：2-s2.0-85151528374)、WOS

基金：AcknowledgmentsThis work was partially supported by the NSFC/RGC Joint Research Scheme (Project nos. 12061160462 and N_CityU102/20) and NSF (Project no. 61877039) of China.

语种：英文

外文摘要：It is known that more and more mathematicians have paid their attention to the field of learning with a Banach space since Banach spaces may provide abundant inner-product structures. We give investigations on the convergence of a kernel-regularized online binary classification learning algorithm in the setting of reproducing kernel Banach spaces (RKBSs), design an online iteration algorithm with the subdifferential of the norm and the logistic loss, and provide an upper bound for the learning rate, which shows that the online learning algorithm converges if RKBSs satisfy 2-uniform convexity.