中文摘要：

提出了一种基于数据深度加权的鲁棒性支撑向量机。针对支撑向量机对噪声或离群点的高敏感性，在其优化函数的正则项中增加深度因子，以针对性地弱化这些点对分类结果的影响，使得分类更具有鲁棒性。给出了1范数和2范数约束下的深度加权支撑向量机的具体形式，并推导了空间秩深度在特征空间中的求解方式。相对于文献中的中心距加权方式，该方法抗噪声能力更强。

英文摘要：

This paper presents a robust support vector machine （SVM） weighted by the spatial rank depth. According to the high sensitivity of SVM to the outliers, the regularization term is weighted by the data depth factor in order to adaptively reduce the influence on the classification by those outliers. The two dual forms of 1-norm and 2-norm depth weighted SVM （DWSVM） are deduced respectively, and the solution of depth in feature space is also presented. Compared with the method weighted by the distance to the mean center, this method is more robust than fuzzy SVM.