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《Journal of Air Force Engineering University(Natural Science Edition)》 2008-04
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An Incremental Training Algorithm of SVM Based on the Distance Ratio

XU Hai-long,WANG Xiao-dan,SHI Zhao-hui,HUA JI-xue,Quan Wen(Missile Institute,Air Force Engineering University,Sanyuan 713800,Shaanxi,China)  
Due to the good learning and generalization performance,the SVM(support vector machine) has been widely used in practice.But,how to make the SVM more effectively perform incremental learning is a problem that needs to be solved in the present application of the SVM.The distribution characteristics of Support vectors are studied and a novel improved incremental SVM learning algorithm-distance ratio algorithm is proposed.According to the removing rules of the proposed method,an appropriate parameter is set and samples that have less effect on later training are abandoned.According to the definition in distance ratio algorithm,the ratio between the center distance of each sample and the distance of each to the optimum classification surface is calculated.In this way,the training data sets can be effectively reduced.Experiment on standard data sets shows that by using this method the classification accuracy can be guaranteed and the training speed can be effectively improved.
【Fund】: 国家自然科学基金资助项目(50505051);; 陕西省自然科学研究计划项目(2007F19)
【CateGory Index】: TP391.41
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