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《Acta Electronica Sinica》 2001-05
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An Analysis of SVMs Generalization Performance

ZHOU Wei da,ZHANG Li,JIAO Li cheng (National Key Lab.for Radar Signal Processing,Xidian University,Xi'an 710071,China)  
Some geometry of Support Vector Machines for classification and regression is described and proven.And then the generalization performance of SVMs on newly added samples is discussed.Through the analysis of the property of newly added samples and the effect of them on support vectors and non support vectors,some valuable results are presented.These enable us to conclude that SVM has a good compatibility,adaptability and generalization performance for newly added samples and is a hereditable learning model.
【Fund】: 国家“8 63”项目! (No.863 30 6 0 6 0 6 1 );; 教育部博士点基金
【CateGory Index】: TP181
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