Support Vector Regression via a Combined Reward Cum Penalty Loss Function
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2019
- DOI:
- 10.48550/arXiv.1904.12331
- arXiv:
- arXiv:1904.12331
- Bibcode:
- 2019arXiv190412331A
- Keywords:
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- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- For any assistance , reader can contact on email with Pritam Anand. Email id - ltpritamanand@gmail.com. The valuable opinion/comments on the work are welcomed. Looking for collaboration especially for speeding up the solution of optimization problems