上海财经大学统计与管理学院张岭松教授作了一场题为“Distance-weighted Support Vector Machine(距离加权支持向量机)”的讲座，统计与管理学院主要的学科专业是统计学，包括经济管理统计、金融统计、统计理论与方法、数量金融与风险管理等多个学科方向。上海财经大学统计学科是一个历史悠久、成绩斐然的学科。讲座的主要内容是：

　　这既拥有支持向量机(SVM)和距离加权歧视(DWD)的优点的新型线性分类方法在这篇文章中提出。所提出的距离加权支持向量机的方法可以被看作是支持向量机的混合和DWD即通过最小化主要DWD损失发现分类方向，并确定在SVM方式截距项。我们证明了我们的方法inheres DWD的优点，因此，克服了SVM的数据打桩及过拟合问题。另一方面，新的方法是不受不平衡数据问题，它是在DWD SVM的一个主要优点。它使用一个不寻常的损失相结合的枢纽损失(SVM)，并通过腋窝超平面的招DWD损失。几个理论性能，包括费舍尔的一致性和DWSVM解决方案的渐近正态发展。我们使用一些模拟的例子表明，新方法可以同时分类性能和可解释性竞争DWD和SVM。一个真正的数据应用进一步确立了我们方法的有效性。

　　原文：A novel linear classification method that possesses the merits of both the Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is proposed in this article. The proposed Distance-weighted Support Vector Machine method can be viewed as a hybrid of SVM and DWD that finds the classification direction by minimizing mainly the DWD loss, and determines the intercept term in the SVM manner. We show that our method inheres the merit of DWD, and hence, overcomes the data-piling and overfitting issue of SVM. On the other hand, the new method is not subject to imbalanced data issue which was a main advantage of SVM over DWD. It uses an unusual loss which combines the Hinge loss (of SVM) and the DWD loss through a trick of axillary hyperplane. Several theoretical properties, including Fisher consistency and asymptotic normality of the DWSVM solution are developed. We use some simulated examples to show that the new method can compete DWD and SVM on both classification performance and interpretability. A real data application further establishes the usefulness of our approach.