  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.
