3 papers
stat.ML2019
Comparison theorems on large-margin learning
Jun Fan, Dao-Hong Xiang
This paper studies binary classification problem associated with a family of loss functions called large-margin unified machines (LUM), which offers a natural bridge between distri…
stat.ML2017
Total stability of kernel methods
Andreas Christmann, Daohong Xiang, Ding-Xuan Zhou
Regularized empirical risk minimization using kernels and their corresponding reproducing kernel Hilbert spaces (RKHSs) plays an important role in machine learning. However, the ac…
stat.ML2016
A short note on extension theorems and their connection to universal consistency in machine learning
Andreas Christmann, Florian Dumpert, Dao-Hong Xiang
Statistical machine learning plays an important role in modern statistics and computer science. One main goal of statistical machine learning is to provide universally consistent a…