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论文类型：会议论文

发表时间：2011-01-01

收录刊物：CPCI-S

卷号：50-51

页面范围：669-+

关键字：min-max-min programming; aggregate function; homotopy method; weak-normal cone condition; multiple-instance classification

摘要：Min-max-min programming is an important but difficult nonsmooth programming. An aggregate homotopy method was given for solving min-max-min programming by Bo Yu el al. However, the method requires a difficult to verify weak-normal cone condition. Moreover, the method is only theoretically with no algorithmic implementation. In this paper, the weak normal cone condition is discussed first. A class of min-max-min programming satisfying the condition is introduced. A detailed algorithm to implement the method is presented. Models arising from some applications such as support vector machine for multiple-instance classification in data mining, can be included in the problem. In the end, the aggregate homotopy method is given to solve the multiple-instance support vector machine model.