Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions
存在异常值时的提升学习：基于非凸损失的自适应分类
This article examines the role and the efficiency of nonconvex loss functions for binary classification problems. In particular, we investigate how to design adaptive and effective boosting algorithms that are robust to the presence of outliers in the data or to the presence of errors in the observed data labels. We demonstrate that nonconvex losses play an important role for prediction accuracy because of the diminishing gradient properties—the ability of the losses to…