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

Abstract

A new smoothing method for solving ? -support vector regression (?-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 ?-insensitive support vector regression. We term this redeveloped problem as ?-smooth support vector regression (?-SSVR). The performance and predictive ability of ?-SSVR are investigated and compared with other methods such as LIBSVM (?-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 ?- SSVR model is comparable or superior to those obtained by LIBSVM and P-SVM. The results indicate that ?-SSVR can be used as an alternative powerful modeling method for regression studies. The experimental results show that the presented algorithm ?-SSVR, plays better precisely and effectively than LIBSVMand P-SVM in predicting antitubercular activity.