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《Journal of East China University of Science and Technology》 2002-05
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Support Vector Machine and Its Applications to Function Approximation

ZHU Guo qiang 1,2 , LIU Shi rong 1* , YU Jin shou 2 (1.Research Institute of Electrical Engineering and Automation, Ningbo Universit y, Ningbo 315211, China; 2.Research Institute of Automation ECUST, Shanghai 20 0237, China)  
Support vector machine is a new machine learning algorith m, based theoretically on statistic learning theory created by Vapnik. Employing the criteria of structural risk minimization, which minimizes the errors betwee n sample data and model data and decreases simultaneously the upper bound of p redict error of model, SVM's generalization is better than others. The character istics of SVM, such as the strong learning capability based on small samples, th e good characteristic of generalization and insensitivity to random noise distur bance, are shown by its applications to function approximation.
【Fund】: 宁波市科技攻关项目 (0 0 12 0 0 2 )
【CateGory Index】: TP181
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