Two Multi-class Lagrangian Support Vector Machine Algorithms

Authors:
Hua Duan;Quanchang Liu;Guoping He;Qingtian Zeng
Affiliations:
Department of Mathematics, Shanghai Jiaotong University, Shanghai 200240, P.R. China and College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 2665 ...;College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, P.R. China;College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, P.R. China;College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, P.R. China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

Support vector machines (SVMs) were designed for two-class classification problems, and multi-class classification problems have been solved by combining independently produced two-class decision functions. In this paper, we propose two multi-class Lagrangian Support Vector Machine(LSVM) algorithms using the quick and simple properties of LSVM. The experimental results in the linear and nonlinear cases indicate that the CPU running time of these two algorithms is shorter than that of the standard support vector machines, and their training correctness and testing correctness are almost identical.