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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Exploiting Uncertain Data in Support Vector Classification
verfasst von : Jianqiang Yang, Steve Gunn
Erschienen in: Knowledge-Based Intelligent Information and Engineering Systems
Verlag: Springer Berlin Heidelberg
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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.