DocumentCode :
2075889
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
fYear :
2010
fDate :
3-5 Nov. 2010
Firstpage :
1
Lastpage :
5
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;
fLanguage :
English
Publisher :
ieee
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
Type :
conf
DOI :
10.1109/ITAB.2010.5687779
Filename :
5687779
Link To Document :
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