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
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;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1380924