Abstract
Support Vector Machines (SVMs) are now thought as a powerful method for solving pattern recognition problems. SVMs are usually formulated as Quadratic Programming (QP). Using another distance function, SVMs can be formulated as Linear Programming (LP). In general, SVMs tend to make overlearning. In order to overcome this difficulty, the notion of soft margin is introduced. In this event, it is difficult to decide the weight for slack variables reflecting soft margin. In this paper, soft margin method is extended to Multi Objective Linear Programming (MOLP). It will be shown throughout several examples that SVMs reformulated as MOLP can give a good performance in pattern classification.
