Support Vector Machines versus Decision Templates in Biomedical Decision Fusion

Information fusion is drawing increasing interest in many application contexts, especially in biomedical decision making. In this work, we provide a framework for addressing the statistical performance of the decision fusion layer. The decision templates (DTs) fusion method is examined as a distance based combiner and statistically compared with an SVM discriminant hyper-classifier. Our aim is broader than providing experimental results on the performance of the two fusion schemes. We attempt to highlight the theoretical advantages of support vectors as multiple attractor points in a hyper-classifier¿s feature space. Moreover we show that the use of SVMs in this task is an extensible framework that can be adapted to the problem formulation.