Support Vector Regression via a Combined Reward Cum Penalty Loss Function
This is an incremental improvement for regression tasks, potentially enhancing support vector regression performance.
The authors tackled regression by introducing a combined reward cum penalty loss function that penalizes points outside and rewards points inside an ε-tube, resulting in a new RP-ε-SVR model with properties validated experimentally.
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 $ε$-tube of the regressor and also assigns reward for the data points which lie inside of the $ε$-tube of the regressor. The combined reward cum penalty loss function based regression (RP-$ε$-SVR) model has several interesting properties which are investigated in this paper and are also supported with the experimental results.