Perturbation to enhance support vector machines for classification

Authors:
K. N. To;C. C. Lim
Affiliations:
School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide 5005, Australia;School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide 5005, Australia
Venue:
Journal of Computational and Applied Mathematics - Special issue on proceedings of the international symposium on computational mathematics and applications
Year:
2004

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Abstract

This paper presents a perturbation method to obtain a sensitivity measure of trained solution from a set of input patterns using support vector machine learning. Applying to classify images of binary classes, the sensitivity measure adds ability to extract content of interest of images for two classes in image classification.