Enhancing Generalization Capability of SVM Classifiers with Feature Weight Adjustment

It is well recognized that support vector machines (SVMs) would produce better classification performance in terms of generalization power. A SVM constructs an optimal separating hyper-plane through maximizing the margin between two classes in high-dimensional feature space. Based on statistical learning theory, the margin scale reflects the generalization capability to a great extent. The bigger the margin scale takes, the better the generalization capability of SVMs will have. This paper makes an attempt to enlarge the margin between two support vector hyper-planes by feature weight adjustment. The experiments demonstrate that our proposed techniques in this paper can en-hance the generalization capability of the original SVM classifiers.