%0 Journal Article %1 noauthororeditor %A Doreswamy, %A .M.Vastrad, Chanabasayya %D 2013 %J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), %K LIBSVM Newton-Armijo P-SVM ε-SSVR %N 2 %P 14 %R 10.5121/ijscai.2013.2204 %T 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 %U http://airccse.org/journal/ijscai/papers/0413scai04.pdf %V 2 %X 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

@article{noauthororeditor, abstract = { 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}, added-at = {2018-04-26T05:31:57.000+0200}, author = {Doreswamy and .M.Vastrad, Chanabasayya}, biburl = {https://www.bibsonomy.org/bibtex/2607361c5c0aa6f9db31ebcae706caa32/leninsha}, doi = {10.5121/ijscai.2013.2204}, interhash = {168d85cea9b9a5b625b22a1a41ff799c}, intrahash = {607361c5c0aa6f9db31ebcae706caa32}, issn = {2319 - 1015}, journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI),}, keywords = {LIBSVM Newton-Armijo P-SVM ε-SSVR}, language = {English}, month = {April}, number = 2, pages = 14, timestamp = {2018-04-26T05:31:57.000+0200}, title = {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 }, url = {http://airccse.org/journal/ijscai/papers/0413scai04.pdf}, volume = 2, year = 2013 }