Universum learning for SVM regression
Sauptik Dhar, Vladimir Cherkassky · 2017
This paper extends the idea of Universum learning 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 samples, 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.