Journal of the Korean Data and Information Science Society
- 제17권2호
- /
- Pages.467-474
- /
- 2006
- /
- 1598-9402(pISSN)
Weighted Support Vector Machines for Heteroscedastic Regression
초록
In this paper we present a weighted support vector machine(SVM) and a weighted least squares support vector machine(LS-SVM) for the prediction in the heteroscedastic regression model. By adding weights to standard SVM and LS-SVM the better fitting ability can be achieved when errors are heteroscedastic. In the numerical studies, we illustrate the prediction performance of the proposed procedure by comparing with the procedure which combines standard SVM and LS-SVM and wild bootstrap for the prediction.