  In this paper, we introduce a novel combined reward cum penalty loss function
to handle the regression problem. The proposed combined reward cum penalty loss
function penalizes the data points which lie outside the $\epsilon$-tube of the
regressor and also assigns reward for the data points which lie inside of the
$\epsilon$-tube of the regressor. The combined reward cum penalty loss function
based regression (RP-$\epsilon$-SVR) model has several interesting properties
which are investigated in this paper and are also supported with the
experimental results.
