Statistical Inference for Smoothed Support Vector Machines in High Dimensions: From Offline to Online Data
高维平滑支持向量机的统计推断：从离线到在线数据
High-dimensional classification problems often rely on the Lasso-penalized linear Support Vector Machines (SVMs). However, the double non-smoothness induced by the hinge loss and Lasso penalty in this model makes statistical inference challenging and impedes computational efficiency. In this paper, we propose a unified inference framework in both offline and online settings. In the offline case, by applying a convolution smoothing technique to the hinge loss, we construct a…