Support vector machines versus decision templates in biomedical decision fusionSupport vector machines versus decision templates in biomedical decision fusion Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-10-242008enInformation 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.http://creativecommons.org/licenses/by/4.0/625 - 630 Seventh International Conference on Machine Learning and Applications, ICMLA Zervakis Michalis Ζερβακης Μιχαλης Dī́mou, Giánnīs, 1944- Institute of Electrical and Electronics Engineers Biomedical