A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs

S. Sathiya Keerthi; Dennis DeCoste

This paper develops a fast method for solving linear SVMs with L₂ loss function that is suited for large scale data mining tasks such as text classification. This is done by modifying the finite Newton method of Mangasarian in several ways. Experiments indicate that the method is much faster than decomposition methods such as SVM^light, SMO and BSVM (e.g., 4-100 fold), especially when the number of examples is large. The paper also suggests ways of extending the method to other loss functions such as the modified Huber's loss function and the L₁ loss function, and also for solving ordinal regression.

A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs

Abstract