A Combined Reward-Penalty Loss Function Based Support Vector Machine

In this paper, we introduce a novel combined reward cum penalty loss function to handle the classification problem. The proposed Reward cum Penalty loss function based Support Vector Machine (RP-SVM) penalizes those data points which do not lie on the desired location and assigns reward for those data points which lie on the desired location. Unlike C-SVM, the RP-SVM can utilize the full information contained in the training set which makes it to own better generalization ability.