The probabilistic constraints in the support vector machine

摘要

In this paper, a new support vector machine classifier with probabilistic constrains is proposed which presence probability of samples in each class is determined based on a distribution function. Noise is caused incorrect calculation of support vectors thereupon margin can not be maximized. In the proposed method, constraints boundaries and constraints occurrence have probability density functions which it help for achieving maximum margin. Experimental results show superiority of the probabilistic constraints support vector machine (PC-SVM) relative to standard SVM.