Exploiting uncertain data in support vector classification

Authors:
Jianqiang Yang;Steve Gunn
Affiliations:
School of Electronics and Computer Science, University of Southampton, Southampton, UK;School of Electronics and Computer Science, University of Southampton, Southampton, UK
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

Citing 2
Cited 1

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Second Order Cone Programming Approaches for Handling Missing and Uncertain Data

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Probabilistic support vector machines for classification of noise affected data

Information Sciences: an International Journal

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Abstract

A new approach of input uncertainty classification is proposed in this paper. This approach develops a new technique which extends the support vector classification (SVC) by incorporating input uncertainties. Kernel functions can be used to generalize this proposed technique to non-linear models and the resulting optimization problem is a second order cone program with a unique solution. Results are shown to demonstrate how the technique is more robust when uncertainty information is available.