DocumentCode :
2324517
Title :
Least squares support vector machine ensemble
Author :
Bing-Yu Sun ; De-Shuang Huang
Author_Institution :
Hefei Institute of Intelligent Machines, Chinese Academy of Sciences
Volume :
3
fYear :
2004
fDate :
25-29 July 2004
Firstpage :
2013
Abstract :
The LS-SVM ensemble is proposed to improve the performance of the single LS-SVM. During the constructing of the LS-SVM ensemble, bagging algorithm is used because it is more suitable than boosting algorithm in high noise regime. Furthermore, in This work a novel aggregation method of the LS-SVM ensemble is also proposed. Traditionally the aggregation of the ensemble always uses all the available individual LS-SVM, while our approach can exclude the ones which may degrade the performance of the ensemble. Finally, the simulating results demonstrate the effectiveness and efficiency of our approach.
Keywords :
least squares approximations; optimisation; pattern classification; support vector machines; SVM ensemble; aggregation method; bagging algorithm; boosting algorithm; least squares support vector machine; optimisation; pattern classification; Automation; Cost function; Degradation; Erbium; Lagrangian functions; Least squares methods; Machine intelligence; Neural networks; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-8359-1
Type :
conf
DOI :
10.1109/IJCNN.2004.1380924
Filename :
1380924
Link To Document :
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