DocumentCode :
478076
Title :
A Maximum Class Distance Support Vector Machine
Author :
Sun, Zheng ; Zhang, Xiao-guang ; Ren, Shi-jin ; Ruan, Dian-xu
Author_Institution :
Coll. of Mech. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou
Volume :
2
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
13
Lastpage :
17
Abstract :
A maximum class distance based support vector machine classification algorithm (MCDSVM) using Fisher linear discriminant analysis (FLDA) is presented in this paper. The algorithm can maximize the margin between the separating hyperplane and the distance between the samples of two classes. The direction of separating hyperplane can be consistent with the distribution of samples and the algorithm can achieve higher classification accuracy. This algorithm can also overcome the over-fitting of SVM resulting from outliers, as well as the problem that the hyperplane doesn´t adapt to the distribution of samples. The principle and realization of the algorithm are addressed in detail in this paper and the classification performance is also analyzed in theory. Finally, a simulation demonstrates the efficiency of this new algorithm.
Keywords :
pattern classification; statistical analysis; support vector machines; Fisher linear discriminant analysis; SVM; maximum class distance based support vector machine classification algorithm; Algorithm design and analysis; Classification algorithms; Data mining; Educational institutions; Linear discriminant analysis; Machine learning algorithms; Statistical learning; Sun; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.282
Filename :
4666947
Link To Document :
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