Article ID: | iaor20071457 |
Country: | Germany |
Volume: | 1 |
Issue: | 3/4 |
Start Page Number: | 211 |
End Page Number: | 230 |
Publication Date: | Oct 2004 |
Journal: | Computational Management Science |
Authors: | Tanino Tetsuzo, Nakayama Hirotaka, Asada Takeshi, Yun Yeboon |
Keywords: | programming: multiple criteria |
Support Vector Machines (SVMs) are now very popular as a powerful method in pattern classification problems. One of main features of SVMs is to produce a separating hyperplane which maximizes the margin in feature space induced by nonlinear mapping using kernel function. As a result, SVMs can treat not only linear separation but also nonlinear separation. While the soft margin method of SVMs considers only the distance between separating hyperplane and misclassified data, we propose in this paper multi-objective programming formulation considering surplus variables. A similar formulation was extensively researched in linear discriminant analysis mostly in 1980s by using Goal Programming (GP). This paper compares these conventional methods such as SVMs and GP with our proposed formulation through several examples.