A generalized-space expansion of support vector machines for diagnostic systemsA generalized-space expansion of support vector machines for diagnostic systems Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-10-252010enSupport Vector Machines (SVMs) are by now an established tool used in state of the art applications in the biomedical domain. Their prevalence has unveiled both a very effective generalization capability and the inherent positive definiteness constraints in kernel selection. In this work we apply a series of composite kernel extensions stemming from nonlinear second-level kernels to standard diagnostic problems. Our aim is twofold. Firstly, to create a formulation that can accept arbitrary non-positive definite feature kernels and secondly, to allow for nonlinear second-level kernels as part of this scheme.http://creativecommons.org/licenses/by/4.0/1-510th IEEE International Conference on Information Technology and Applications in Biomedicine Dimou Ioannis Δημου Ιωαννης Zervakis Michail Ζερβακης Μιχαηλ Institute of Electrical and Electronics Engineers Support vector machines Medical diagnostic computing Breast Cancer Generalized-space expansion Diagnostic systems Generalization capability Positive definiteness constraints Kernel selection Composite kernel extensions Nonlinear second-level kernels