文献类型：期刊文献

中文题名：LEARNING RATES OF KERNEL-BASED ROBUST CLASSIFICATION

英文题名：Learning Rates of Kernel-Based Robust Classification

作者：Wang, Shuhua[1];Sheng, Baohuai[2,3]

机构：[1]Jingdezhen Ceram Univ, Sch Informat Engn, Jingdezhen 333403, Peoples R China;[2]Zhejiang Yuexiu Univ, Dept Finance, Shaoxing 312030, Peoples R China;[3]Shaoxing Univ, Dept Appl Stat, Shaoxing 312000, Peoples R China

年份：2022

卷号：42

期号：3

起止页码：1173

中文期刊名：数学物理学报：B辑英文版

外文期刊名：ACTA MATHEMATICA SCIENTIA

收录：CSTPCD、、CSCD2021_2022、Scopus、CSCD、PubMed

基金：This work is supported by the NSF (61877039), the NSFC/RGC Joint Research Scheme (12061160462 and N CityU 102/20) of China, the NSF (LY19F020013) of Zhejiang Province, the Special Project for Scientific and Technological Cooperation (20212BDH80021) of Jiangxi Province, the Science and Technology Project in Jiangxi Province Department of Education (GJJ211334).

语种：英文

中文关键词：Support vector machine;robust classification;quasiconvex loss function;learning rate;right-sided directional derivative

外文关键词：Support vector machine; robust classification; quasiconvex loss function; learning rate; right-sided directional derivative

中文摘要：This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the performance deterioration caused by the outliers.A comparison relationship between the excess misclassification error and the excess generalization error is provided;from this,along with the convex analysis theory,a kind of learning rate is derived.The results show that the performance of the classifier is effected by the outliers,and the extent of impact can be controlled by choosing the homotopy parameters properly.

外文摘要：This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the performance deterioration caused by the outliers. A comparison relationship between the excess misclassification error and the excess generalization error is provided; from this, along with the convex analysis theory, a kind of learning rate is derived. The results show that the performance of the classifier is effected by the outliers, and the extent of impact can be controlled by choosing the homotopy parameters properly.