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.
作者
王淑华
盛宝怀
Shuhua WANG;Baohuai SHENG(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)
基金
supported by the NSF(61877039)
the NSFC/RGC Joint Research Scheme(12061160462 and N City U 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)。
