Study Of E-Smooth Support Vector Regression And Comparison With E- Support Vector Regression And Potential Support Vector Machines For Prediction For The Antitubercular Activity Of Oxazolines And Oxazoles Derivatives

A new smoothing method for solving ε -support vector regression (ε-SVR), tolerating a small error in fitting a given data sets nonlinearly is proposed in this study. Which is a smooth unconstrained optimization reformulation of the traditional linear programming associated with a ε-insensitive support vector regression. We term this redeveloped problem as ε-smooth support vector regression (ε-SSVR). The performance and predictive ability of ε-SSVR are investigated and compared with other methods such as LIBSVM (ε-SVR) and P-SVM methods. In the present study, two Oxazolines and Oxazoles molecular descriptor data sets were evaluated. We demonstrate the merits of our algorithm in a series of experiments. Primary experimental results illustrate that our proposed approach improves the regression performance and the learning efficiency. In both studied cases, the predictive ability of the εSSVR model is comparable or superior to those obtained by LIBSVM and P-SVM. The results indicate that ε-SSVR can be used as an alternative powerful modeling method for regression studies. The experimental results show that the presented algorithm ε-SSVR, , plays better precisely and effectively than LIBSVMand P-SVM in predicting antitubercular activity.