Ameneh Arjmandzadeh - Department of Mathematics, Islamic Azad University of Bojnourd Branch, Bojnourd, Iran Sohrab Effati - Department of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran Mohammad Zamirian - Department of Mathematics, Islamic Azad University of Bojnourd Branch, Bojnourd, Iran
Support vector machines (SVMs) have been widely applied in regression analysis. In this paper, the application of SVM in regression for interval samples is proposed. The standard support vector regression (SVR), is a quadratic optimization problem that is formulated according to the form of training samples and optimal hyperplane is obtained. In real world, the parameters are seldom known and usually are estimated. In this paper we propose an interval support vector regression (ISVR) problem which the training samples are interval values. Using duality theorem and applying variable transformation theorem the problem is solved and two hyperplanes correspond to the upper bound and the lower bound of solution set is obtained. Efficiency of our approach is confirmed by a numerical example.
Ameneh Arjmandzadeh, Sohrab Effati, Mohammad Zamirian, Interval Support Vector Machine in Regression Analysis, Journal of Mathematics and Computer Science, 2 (2011), no. 3, 565--571
Arjmandzadeh Ameneh, Effati Sohrab, Zamirian Mohammad, Interval Support Vector Machine in Regression Analysis. J Math Comput SCI-JM. (2011); 2(3):565--571
Arjmandzadeh, Ameneh, Effati, Sohrab, Zamirian, Mohammad. "Interval Support Vector Machine in Regression Analysis." Journal of Mathematics and Computer Science, 2, no. 3 (2011): 565--571
