A New Support Vector Machine for Data Mining

Abstract. This paper proposes a new support vector machine (SVM) with a robust loss function for data mining. Its dual optimal formation is also constructed. A gradient based algorithm is designed for fast and simple implementation of the new support vector machine. At the same time it analyzes algorithm’s convergence condition and gives a formula to select learning step size. Numerical simulation results show that the new support vector machine performs significantly better than a standard support vector machine.

• Type: Conference
• Year: 2005
• Where: ADMA
• Authors: Haoran Zhang, Xiaodong Wang, Changjiang Zhang, Xiuling Xu
• Keywords: ADMA 2005, Data Mining, Dual Optimal Formation, Standard Support Vector, Support Vector Machine