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