  Support vector machines (SVM) is one of the well known supervised classes of
learning algorithms. Furthermore, the conic-segmentation SVM (CS-SVM) is a
natural multiclass analogue of the standard binary SVM, as CS-SVM models are
dealing with the situation where the exact values of the data points are known.
This paper studies CS-SVM when the data points are uncertain or mislabelled.
With some properties known for the distributions, a chance-constrained CS-SVM
approach is used to ensure the small probability of misclassification for the
uncertain data. The geometric interpretation is presented to show how CS-SVM
works. Finally, we present experimental results to investigate the chance
constrained CS-SVM's performance.
