Support Vector Regression via a Combined Reward Cum Penalty Loss Function

Pritam Anand; Reshma Rastogi; Suresh Chandra

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arXiv · 1904.12331

Support Vector Regression via a Combined Reward Cum Penalty Loss Function

Pritam Anand·Reshma Rastogi·Suresh Chandra

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Abstract

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.

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Pritam Anand, Reshma Rastogi, Suresh Chandra. 2019-04-28. Support Vector Regression via a Combined Reward Cum Penalty Loss Function. https://arxiv.org/abs/1904.12331

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