Learning with smooth Hinge losses

Due to the non-smoothness of Hinge loss in SVM, it is difficult obtain a faster convergence rate with modern optimization algorithms. In this paper, we introduce two smooth losses ψG(α;σ) and ψM(α;σ) which are infinitely differentiable converge uniformly α as σ tends 0. By replacing these losses, support vector machines (SSVMs), respectively. Solving SSVMs Trust Region Newton method (TRON) leads quadratically convergent Experiments text classification tasks show that proposed effective real-world applications. We also general convex function unify several commonly-used functions machine learning. The framework provides approximation non-smooth functions, can be used models solved