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 b...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.展开更多
基金supported by the NSF(61877039)the NSFC/RGC Joint Research Scheme(12061160462 and N City U 102/20)of China+2 种基金the NSF(LY19F020013)of Zhejiang Provincethe Special Project for Scientific and Technological Cooperation(20212BDH80021)of Jiangxi Provincethe Science and Technology Project in Jiangxi Province Department of Education(GJJ211334)。
文摘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.
