Accelerating SVM on large scale regression problem
Liu Yongping, Qiu Dongtao · 2004
The support vector machine (SVM) is a new generation learning system based on recent advances in statistical learning theory. There is evidence showing that SVMs can deliver state-of-the art performance in real-world applications such as text categorisation, handwritten character recognition, image classification, biosequence analysis, etc. In this paper, we investigated the large scale regression problem by using SVMs, but instead of solving quadratic programming slowly, we try to accelerate the regression speed by linear programming formulation using 1-norm or /spl infin/-norm for the model complexity. Computational experiments show that linear programming gives us very good performance without deteriorating the results.