DocumentCode
1641476
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
fYear
2007
Firstpage
510
Lastpage
512
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/CHICC.2006.4346931
Filename
4346931
Link To Document