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A new smoothing method for solving ε support vector regression (εSVR), 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 εinsensitive support
vector regression. We term this redeveloped problem as εsmooth support vector regression (εSSVR).
The performance and predictive ability of εSSVR are investigated and compared with other methods
such as LIBSVM (εSVR) and PSVM 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 PSVM. The results indicate
that εSSVR can be used as an alternative powerful modeling method for regression studies. The
experimental results show that the presented algorithm εSSVR, , plays better precisely and effectively
than LIBSVMand PSVM in predicting antitubercular activity
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