Incorporating prior knowledge with weighted margin support vector machines
Wu Xiaoyun, Rohini K. Srihari · 2004
Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus a larger training dataset is required for better performance. In some applications, there might be human knowledge available that, in principle, could compensate for the lack of data. In this paper, we propose a simple generalization of SVM: Weighted Margin SVM (WMSVMs) that permits the incorporation of prior knowledge. We show that Sequential Minimal Optimization can be used in training WMSVM. We discuss the issues of incorporating prior knowledge using this rather general formulation. The experimental results show that the proposed methods of incorporating prior knowledge is effective.