Support Vector Regression with Imprecise Observations

Support vector classification (SVC) and support vectors regression (SVR) are learning machines that have excellent generalization performance. The data which used by classical statistical learning theory is assumed precise. However, the data from real world sometimes low-quality or imprecise, the uncertainty theory and uncertain statistics are appropriate methods to process the imprecise observations. In this paper, the optimal hyperplane under the framework of uncertainty theory be put forward