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 -tube of the regressor and also assigns reward for the data points which lie inside of the -tube of the regressor. The combined reward cum penalty loss function based regression (RP--SVR) model has several interesting properties which are investigated in this paper and are also supported with the experimental results.
Cite
@article{arxiv.1904.12331,
title = {Support Vector Regression via a Combined Reward Cum Penalty Loss Function},
author = {Pritam Anand and Reshma Rastogi and Suresh Chandra},
journal= {arXiv preprint arXiv:1904.12331},
year = {2020}
}
Comments
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