Weighted linear loss twin support vector machine for large-scale classification

In this paper, we formulate a twin-type support vector machine for large-scale classification problems, called weighted linear loss twin support vector machine (WLTSVM). By introducing the weighted linear loss, our WLTSVM only needs to solve simple linear equations with lower computational cost, and meanwhile, maintains the generalization ability. So, it is able to deal with large-scale problems efficiently without any extra external optimizers. The experimental results on several benchmark datasets indicate that, comparing to TWSVM, our WLTSVM has comparable classification accuracy but with less computational time.