سال انتشار: ۱۳۸۶
محل انتشار: اولین کنگره مشترک سیستم های فازی و سیستم های هوشمند
تعداد صفحات: ۱۰
Hadi Sadoghi Yazdi – Engineering Department, Tarbiat Moallem University of Sabzevar, Sabzevar, Iran
Sohrab Effati – Department of Mathematics, Tarbiat Moallem University of Sabzevar, Sabzevar, Iran
images. A new support vector machine classifier is presented with probabilistic constrains which presence probability of samples in each class is determined based on a distribution function. Noise is caused to found incorrect support vectors thereupon margin can not be maximized. In the proposed method, constraints boundaries and constraints occurrence have probability density functions which it helps for achieving maximum margin. Experimental results in the machine identification shows superiority of the probabilistic constraints support vector machine (PC-SVM) relative to standard SVM.