Robust Unsupervised and Semi-supervised Bounded v - Support Vector Machines

Support Vector Machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semisupervised classification algorithms based on Bounded C-SVMs, Bounded v-SVMs, Lagrangian SVMs (LSVMs) and robust version to Bounded C-SVMs respectively, which are relaxed to Semi-definite Programming (SDP), get good classification results. But the parameter C in Bounded C-SVMs SVMs no specific in quantification. Therefore we proposed robust version to unsupervised and semi-supervised classification algorithms based on Bounded v- Support Vector Machines (Bv-SVMs). Numerical results confirm the robustness of proposed methods and show that our new algorithms based on robust, version to Bv-SVM often obtain more accurate results than other algorithms.