?-Nonparallel support vector machine for pattern classification
In this paper, we propose a novel nonparallel hyperplane classifier, named ?-nonparallel support vector machine (?-NPSVM), for binary classification. Based on our recently proposed method, i.e., nonparallel support vector machine (NPSVM), which has been proved superior to the twin support vector machines, ?-NPSVM is parameterized by the quantity ? to let ones effectively control the number of support vectors. By combining the ?-support vector classification and the ?-support vector regression together to construct the primal problems, ?-NPSVM inherits the advantages of ?-support vector machine so that enables us to eliminate one of the other free parameters of the NPSVM: the accuracy parameter ? and the regularization constant C. We describe the algorithm, give some theoretical results concerning the meaning and the choice of ?, and also report the experimental results on lots of data sets to show the effectiveness of our method.
nonparallel support vector machine twin support vector machines structural risk minimization principle sparseness