Abstract Aiming at large-scale machine learning problems with noise and interference data,the non-convex Ramp loss function is adopted to suppress the influences of noise and interference data,and a fast learning method is proposed for solving the non-convex linear support vector machines based on stochastic optimization. It effectively improves the training speed and the prediction accuracy. The experimental results manifest that the proposed method greatly reduces the learning time,and on the MNIST dataset the training time is reduced by 4 orders of magnitude compared to the traditional learning method. Meanwhile,it improves the prediction speed in a sense and greatly enhances the generalization performance of the classifiers for noisy dataset.
2013, Vol. 26 
