… , including the support vector machine (SVM). The experiments show that two direct multi-…
The object function is a typical least square problem. The first item controls the fitting error of …

… The feasibility on the application of LIBS to lunar exploration was discussed. … couples
kernel partial least squares(KPLS) feature extraction with support vector regression machine(SVR) …

… To achieve this, we use the ordinary least squares and quantile regression approach, and
find that the patterns of herding and non-herding behavior are significantly different. It seems …

… study, the two stage least square estimation cannot be adopted. On the other hand, as the
data are sufficient, this research proposes to follow Granger (1969) to use lagged dependent …

… The validation results show that the calibration greatly improved the accuracy of ET
products, and the importance of calibration cannot be overemphasized in applications used in …

… The purpose of this study is to apply non-parametric data envelopment analysis (DEA) and
parametric stochastic frontier analysis (SFA) to examine the efficiency situation of correctional …

… Weighted Least Squares-Support Vector Regression)model … the support vector regression
and multiple linear regression model… We use cookies to help provide and enhance our service …