SUPPORT VECTOR REGRESSION WITH RANDOM OUTPUT VARIABLE AND PROBAILISTIC CONSTRAINTS

Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadratic optimization problem. The proposed method is illustrated by several simulated data and real data sets for both models (linear and nonlinear) with probabilistic constraints.