Proximal parametric-margin support vector classifier and its applications

As a development of powerful SVMs, the recently proposed parametric-margin nu-support vector machine (par-nu-SVM) is good at dealing with heteroscedastic noise classification problems. In this paper, we propose a novel and fast proximal parametric-margin support vector classifier (PPSVC), based on the par-nu-SVM. In the PPSVC, we maximize a novel proximal parametric-margin by solving a small system of linear equations, while the par-nu-SVM maximizes the parametric-margin by solving a quadratic programming problem. Therefore, our PPSVC not only is useful with the case of heteroscedastic noise but also has a much faster learning speed compared with the par-nu-SVM. Experimental results on several artificial and public available datasets show the advantages of our PPSVC both on the generalization ability and learning speed. Furthermore, we investigate the performance of the proposed PPSVC on the text categorization problem. The experimental results on two benchmark text corpora show the practicability and effectiveness of the proposed PPSVC.