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《Journal of Hunan University(Natural Sciences)》 2007-09
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Boosting Ensemble of Support Vector Regression for Landslide Prediction

DONG Hui1,FU He-lin1,LENG Wu-ming1,LONG Wan-xue2 (1.Civil Architectural Engineering College,Central South Univ,Changsha,Hunan 410075,China;2.Guizhou Provincial Institute of Planning,Prospecting and Designing of Communications Infrastructures,Guiyang,Guizhou 550001,China)  
The regression results of the practically implemented support vector machines(SVR) are often far from the theoretically expected level because of the high complexity of time and space of real data,and it is difficult to practically select hyper-parameters for SVR.To improve the limited regression performance of the real SVR,we proposed a method to form committee machines for regression by using boosting techniques.In the process of algorithm,each sub-SVR was trained independently and the training set was created based on weighted random sampling from the original dataset.The final SVR solution was produced by aggregating the sub-SVRs results through methods such as least-squares estimation based weighting etc.Experiments on both artificial and real-world datasets indicated that the Boosting ensemble SVR outperformed single SVR.In addition,we compared ensembles constructed using boosting with those using bagging,and the results showed that boosting was generally much better.
【Fund】: 贵州省交通建设科技项目(2003-318-80-201);; 交通部西部重点基金资助项目(200331880201)
【CateGory Index】: P642.22
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