上海財經(jīng)大學(xué)統(tǒng)計與管理學(xué)院張嶺松教授作了一場題為“Distance-weighted Support Vector Machine(距離加權(quán)支持向量機)”的講座，統(tǒng)計與管理學(xué)院主要的學(xué)科專業(yè)是統(tǒng)計學(xué)，包括經(jīng)濟管理統(tǒng)計、金融統(tǒng)計、統(tǒng)計理論與方法、數(shù)量金融與風(fēng)險管理等多個學(xué)科方向。上海財經(jīng)大學(xué)統(tǒng)計學(xué)科是一個歷史悠久、成績斐然的學(xué)科。講座的主要內(nèi)容是：

　　這既擁有支持向量機(SVM)和距離加權(quán)歧視(DWD)的優(yōu)點的新型線性分類方法在這篇文章中提出。所提出的距離加權(quán)支持向量機的方法可以被看作是支持向量機的混合和DWD即通過最小化主要DWD損失發(fā)現(xiàn)分類方向，并確定在SVM方式截距項。我們證明了我們的方法inheres DWD的優(yōu)點，因此，克服了SVM的數(shù)據(jù)打樁及過擬合問題。另一方面，新的方法是不受不平衡數(shù)據(jù)問題，它是在DWD SVM的一個主要優(yōu)點。它使用一個不尋常的損失相結(jié)合的樞紐損失(SVM)，并通過腋窩超平面的招DWD損失。幾個理論性能，包括費舍爾的一致性和DWSVM解決方案的漸近正態(tài)發(fā)展。我們使用一些模擬的例子表明，新方法可以同時分類性能和可解釋性競爭DWD和SVM。一個真正的數(shù)據(jù)應(yīng)用進(jìn)一步確立了我們方法的有效性。

　　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.