Universum Learning for SVM Regression

TL;DR

This paper introduces a novel Universum-SVM regression method that leverages additional domain-specific data samples with different distributions to improve regression performance.

Contribution

It extends Universum learning to regression tasks by formulating a new SVM approach that incorporates prior knowledge through Universum data samples.

Findings

Improved regression accuracy demonstrated through empirical comparisons.

Effective integration of Universum data enhances SVM regression performance.

Versatile approach applicable to various regression problems.

Abstract

This paper extends the idea of Universum learning [18, 19] to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples or Universum belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons are presented to illustrate the utility of the proposed approach.