DocumentCode :
478179
Title :
Ensemble Implementations on Diversified Support Vector Machines
Author :
Li, Kunlun ; Dai, Yunna ; Zhang, Wei
Author_Institution :
Coll. of Electron. & Inf. Eng., Hebei Univ., Baoding
Volume :
3
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
180
Lastpage :
184
Abstract :
Support vector machine (SVM) is an effective algorithm in pattern recognition. But usually, standard SVM requires solving a quadratic program (QP) problem. In majority situations, most implementations of SVM are approximate solution to the QP problem. As the approximate solutions cannot achieve the expected performance of SRM theory, it is necessary to research ensemble methods for SVM. Recently, in order to augment the diversities of individual classifiers of SVM, many researchers use random partition with the whole training to form sub-training sets. Therefore the performance of aggregated SVM, which was trained on those subsets, was improved. We proposed the ensemble method based on different implementations of SVM, because they have large diversities by their different implementing methods. The experiment results showed that this method is effectively to improve the aggregated learner´s performance.
Keywords :
pattern recognition; quadratic programming; support vector machines; diversified support vector machines; ensemble implementations; pattern recognition; quadratic program problem; Artificial neural networks; Diversity reception; Educational institutions; Equations; Machine learning; Quadratic programming; Space technology; Support vector machine classification; Support vector machines; Voting; Bagging; Ensemble; LS-SVM; PSVM (proximal SVM); SVM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.197
Filename :
4667126
Link To Document :
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