DocumentCode :
3614101
Title :
Multi-class support vector machine
Author :
V. Franc;V. Hlavac
Author_Institution :
Fac. of Electr. Eng., Czech Tech. Univ., Karlovo, Czech Republic
Volume :
2
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
236
Abstract :
We propose a transformation from the multi-class support vector machine (SVM) classification problem to the single-class SVM problem which is more convenient for optimization. The proposed transformation is based on simplifying the original problem and employing the Kesler construction which can be carried out by the use of properly defined kernel only. The experiments conducted indicate that the proposed method is comparable with the one-against-all decomposition solved by the state-of-the-art sequential minimal optimizer algorithm.
Keywords :
"Support vector machines","Support vector machine classification","Kernel","Training data","Cost function","Cybernetics","Optimization methods"
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2002. Proceedings. 16th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-1695-X
Type :
conf
DOI :
10.1109/ICPR.2002.1048282
Filename :
1048282
Link To Document :
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