稳健的多支持向量机自适应提升算法

AdaBoostSVM容易受到离群点的干扰从而影响到算法的泛化性能.离群点是不反映一般规律的数据点,当被错分的数据含有离群点时,AdaBoostSVM会不断地给离群点赋予很大的权重,进而影响到提升的分类准确率.针对这一问题,提出了RAdaBoostSVM算法,通过对权重过大的误分类样本用和它相邻近的几个样本的中心来代替,有效地减小了离群点对提升效果的影响.与AdaBoostSVM算法相比,RAdaBoostSVM对离群点更加稳健更适合于噪声条件下的分类问题.在基准数据集上的实验结果验证了算法的有效性.

Robust AdaBoost with SVM-Based Component Classifiers

The performance of AdaBoostSVM is easily disturbed by the outliers,and the generalization performance of the algorithm will be influenced.Outliers are the data that do not inflect the general regularity.When many misclassified outliers in the data set,the weights of the outliers usually keep increasing during the training procedue of AdaBoostSVM.Thus the generalization performance of the generated classifier gets worse.Concerning this problem,RAdaBoostSVM is proposed to improve the performance of AdaBoostSV...