近日，我系2015级教育信息技术专业研究生杜佳芝同学的论文被国际期刊Expert Systems with Applications(SCI检索，一区，影响因子: 4.292)录用(2017年6月13日投稿，2018年2月2日接收)。Expert Systems with Applications是计算机应用领域国际权威刊物之一，这也是我系专家系统方面研究的又一阶段性成果。

Jiazhi Du, Weigang Lu*, Xiaohe Wu, Junyu Dong, Wangmeng Zuo. L-SVM: A Radius-margin-based SVM algorithm with LogDet Regularization. Expert Systems With Applications. 2018, 102: 113-125.

Theoretically, support vector machines (SVMs) have general error bounds along a radius-margin ratio, while conventional SVMs consider only the maximization of the margin and ignore the minimization of the radius, which is sensitive to affine data transformations. Thus, conventional SVMs can be improved by controlling both the radius and the margin. Several SVM variants based on radius-margin ratio error bounds have been proposed to integrate the radius and margin. However, most of these either require a diagonal transformation matrix or are computationally expensive to optimize. In this paper, we propose a novel radius-margin-based SVM model with LogDet regularization called L-SVM. In our model, we consider the radius and introduce a negative LogDet term to improve the model accuracy. We also adopt a two-step alternating minimization strategy to obtain an optimal solution, which leads to impressive computational improvements. Our experimental results validate the performance of the L-SVM and show that the L-SVM achieves significantly higher accuracy and efficiency compared to conventional SVMs and some other state-of-the-art radius-margin-based SVM methods. In addition, we apply our proposed L-SVM to solve transaction fraud problems and propose a framework for an L-SVM-based fraud detection system.