TY - GEN
T1 - A comparative study of relevant vector machine and support vector machine in uncertainty analysis
AU - Shi, Yi
AU - Xiong, Fenfen
AU - Xiu, Renqiang
AU - Liu, Yu
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - comparative study
KW - relevant vector machine
KW - reliability analysis
KW - support vector machine
KW - uncertainty analysis
UR - https://www.scopus.com/pages/publications/84890013853
U2 - 10.1109/QR2MSE.2013.6625625
DO - 10.1109/QR2MSE.2013.6625625
M3 - Conference contribution
AN - SCOPUS:84890013853
SN - 9781479910144
T3 - QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
SP - 469
EP - 472
BT - QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
T2 - 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2013
Y2 - 15 July 2013 through 18 July 2013
ER -