A comparative study of relevant vector machine and support vector machine in uncertainty analysis

Relevant Vector Machine (RVM) and Support Vector Machine (SVM) are two relatively new methods that enable us to utilize a few experimental sample points to construct an explicit metamodel. They have been extensively employed in both classification and regression problems. However, their performance in uncertainty analysis is rarely studied. The focus of this paper is to compare the two metamodeling techniques in terms of uncertainty analysis.