DocumentCode :
2489637
Title :
Reduce the number of support vectors by using clustering techniques
Author :
Tran, Quang-Anh ; Zhang, Qian-Li ; Li, Xing
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
Volume :
2
fYear :
2003
fDate :
2-5 Nov. 2003
Firstpage :
1245
Abstract :
A serious problem of support vector machine (SVM) is its low classifying speed. The speed depends on the number of support vectors. The clustering SVM, proposed in this paper, is a new method to reduce the number of support vectors. The method uses a k-means clustering technique to assign the data of each class to k groups, then we train the SVM based on a new dataset consist of the central vectors of each group. The value of k is the upper bound of the number of support vector in the class. Experiment results demonstrate that our method can control the tradeoff between the classifying speed and the performance of SVM.
Keywords :
pattern clustering; support vector machines; central vectors; clustering SVM; k-means clustering technique; support vector machine; support vectors reduction; trade of control; Clustering methods; Cybernetics; Intrusion detection; Machine learning; Size control; Support vector machine classification; Support vector machines; Upper bound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
Type :
conf
DOI :
10.1109/ICMLC.2003.1259678
Filename :
1259678
Link To Document :
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