Indexed by:会议论文

Date of Publication:2011-01-01

Included Journals:CPCI-S

Volume:50-51

Page Number:669-+

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

Abstract: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.