A smoothing multiple support vector machine model

Authors:
Huihong Jin;Zhiqing Meng;Xuanxi Ning
Affiliations:
College of Business and Administration, Nanjing University of Aeronautics and Astronautics, Jiangshu, China;College of Business and Administration, Zhejiang University of Technology, Zhejiang, China;College of Business and Administration, Nanjing University of Aeronautics and Astronautics, Jiangshu, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

In this paper, we study a smoothing multiple support vector machine (SVM) by using exact penalty function. First, we formulate the optimization problem of multiple SVM as an unconstrained and nonsmooth optimization problem via exact penalty function. Then, we propose a two-order differentiable function to approximately smooth the exact penalty function, and get an unconstrained and smooth optimization problem. By error analysis, we can get approximate solution of multiple SVM by solving its approximately smooth penalty optimization problem without constraint. Finally, we give a corporate culture model by using multiple SVM as a factual example. Compared with artificial neural network, the precision of our smoothing multiple SVM which is illustrated with the numerical experiment is better.