An overview on twin support vector machines

摘要

Twin support vector machines (TWSVM) is based on the idea of proximal SVM based on generalized eigenvalues (GEPSVM), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. However, the stand-alone method requires the solution of two smaller quadratic programming problems. This paper mainly reviews the research progress of TWSVM. Firstly, it analyzes the basic theory and the algorithm thought of TWSVM, then tracking describes the research progress of TWSVM including the learning model and specific applications in recent years, finally points out the research and development prospects.