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Support vector machines (SVM) is one of the well known supervised classes oflearning algorithms. Furthermore, the conic-segmentation SVM (CS-SVM) is anatural multiclass analogue of the standard binary SVM, as CS-SVM models aredealing 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-SVMapproach is used to ensure the small probability of misclassification for theuncertain data. The geometric interpretation is presented to show how CS-SVMworks. Finally, we present experimental results to investigate the chanceconstrained CS-SVM's performance.

Chance constrained conic-segmentation support vector machine with uncertain data