典型文献
LEARNING RATES OF KERNEL-BASED ROBUST CLASSIFICATION
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Shuhua WANG;Baohuai SHENG
作者机构：
School of Information Engineering,Jingdezhen Ceramic University,Jingdezhen 333403,China;Department of Finance,Zhejiang Yuexiu University,Shaoxing 312030,China;Department of Applied Statistics,Shaoxing University,Shaoxing 312000,China
文献出处：
引用格式：
[1]Shuhua WANG;Baohuai SHENG-.LEARNING RATES OF KERNEL-BASED ROBUST CLASSIFICATION)[J].数学物理学报（英文版）,2022(03):1173-1190
A类：
LEARNING,RATES,KERNEL,ROBUST,CLASSIFICATION
B类：
OF,BASED,This,paper,considers,robust,kernel,regularized,algorithm,convex,loss,function,which,proposed,alleviate,performance,deterioration,caused,by,outliers,comparison,relationship,between,excess,misclassification,error,generalization,provided,from,this,along,analysis,theory,kind,learning,rate,derived,results,show,that,classifier,effected,extent,impact,can,controlled,choosing,homotopy,parameters,properly
AB值：
0.61955
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