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
Doreswamy, Chanabasayya M.Vastrad · International Journal on Soft Computing Artificial Intelligence and Applications · 2013
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 ε-SSVR 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.