Chance constrained conic-segmentation support vector machine with uncertain data

In classification problems, a classifier is a function that mimics the relationship between the data vectors and their class labels. Support vector machine(SVM) is a popular classifier, which was proposed by Cortes and Vapnik [1] as a maximum margin classifier. The success of the SVM has encouraged further research into extensions to the more general multiclass cases, which has been an active topic of research interest [2-4]. Shilton et al.[5] proposed the conicsegmentation support vector machine (CS-SVM) by introducing the concept of target space into the problem formulation and showed that some other multiclassfication model are special cases of this framework. The standard CS-SVM is dealing with the situation where the exact values of the data points are known.