Exploiting Uncertain Data in Support Vector Classification

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.