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《Science of Surveying and Mapping》 2013-01
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Remotely sensed imagery classification by SVM-based Infinite Ensemble Learning method

YANG Na①②,QIN Zhi-yuan①,ZHANG Jun②(①School of Geomatics,Information Engineering University,Zhengzhou 450052,China;②Troops 65015,Liaoning Dalian 116023,China)  
Support-vector-machines-based Infinite Ensemble Learning method(SVM-based IEL) is one of the ensemble learning methods in the field of machine learning.In this paper,the SVM-based IEL was applied to the classification of remotely sensed imagery besides classic ensemble learning methods such as Bagging,AdaBoost and SVM etc.SVM was taken as the base classifier in Bagging,AdaBoost.The experiments showed that the classic ensemble learning methods have different performances compared to SVM.In detail,the Bagging was capable of enhancing the classification accuracy but the AdaBoost was decreasing the classification accuracy.Furthermore,the experiments suggested that compared to SVM and classic ensemble learning methods,SVM-based IEL has many merits such as increasing both of the classification accuracy and classification efficiency.
【CateGory Index】: TP751;TP18
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