LIU Xian-gui1,XIE un-ming1,CHEN Wu-wei2(1.Departmemt of Mechanical and Power Engineering,Nanchang Institute of Technology,Nanchang 330099,China;2.School of Mechanical and Automotive Engineering,Hefei University of Technology,Hefei 230069,China)
Abstract：Kernel Principal Component analysis(KPCA) has the advantage of extracting nonlinear features.Nonlinear mapping and generalization are the strong capabilities of Support Vector Machine(SVM).they have own advantages when they each is applied into classifica
刘显贵; 谢云敏; 陈无畏. 一种基于核主元分析的支持向量机识别方法[J]. 南昌大学学报（理科版）, 2007, 31(01): 1-.
LIU Xian-gui1,XIE un-ming1,CHEN Wu-wei2(1.Departmemt of Mechanical and Power Engineering,Nanchang Institute of Technology,Nanchang 330099,China;2.School of Mechanical and Automotive Engineering,Hefei University of Technology,Hefei 230069,China). . , 2007, 31(01): 1-.
2007, Vol. 31 