Title :
A generalized-space expansion of Support Vector Machines for diagnostic systems
Author :
Dimou, Ioannis N. ; Zervakis, Michalis E.
Author_Institution :
Electron. & Comput. Eng. Dept, Tech. Univ. of Crete, Chania, Greece
Abstract :
Support 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.
Keywords :
medical diagnostic computing; support vector machines; composite kernel extensions; diagnostic systems; generalization capability; generalized-space expansion; kernel selection; nonlinear second-level kernels; positive definiteness constraints; support vector machines; Books; Breast; Diabetes; Libraries; Support vector machines;
Conference_Titel :
Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on
Conference_Location :
Corfu
Print_ISBN :
978-1-4244-6559-0
DOI :
10.1109/ITAB.2010.5687779