Title :
A Novel Kernel PCA Support Vector Machine Algorithm with Feature Transition Function
Author :
Lianhong, Wang ; Guoyun, Zhang ; Jing, Zhang
Author_Institution :
Hunnan Univ., Changsha
Abstract :
Based on the kernel function, this paper proposes an integrated classification method, combining the support vector machine (SVM) with kernel principle component analysis (KPCA), and its algorithm realization steps are also presented. Simulation experiment results show that the current approach has excellent classification performance, which is suitable for the pattern recognition and eliminate the influence of noise.
Keywords :
pattern classification; principal component analysis; support vector machines; classification performance; feature transition function; kernel principle component analysis; pattern recognition; support vector machine; Algorithm design and analysis; Educational institutions; Equations; Information analysis; Kernel; Pattern recognition; Physics; Principal component analysis; Support vector machine classification; Support vector machines; Classification; KPCA; Kernel Function; Support Vector Machine;
Conference_Titel :
Control Conference, 2007. CCC 2007. Chinese
Conference_Location :
Hunan
Print_ISBN :
978-7-81124-055-9
Electronic_ISBN :
978-7-900719-22-5
DOI :
10.1109/CHICC.2006.4346931
