Support Vector Regression via a Combined Reward Cum Penalty Loss Function
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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.
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