A new support vector machine for data mining

Authors:
Haoran Zhang;Xiaodong Wang;Changjiang Zhang;Xiuling Xu
Affiliations:
College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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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.