Universum learning for SVM regression

Sauptik Dhar, Vladimir Cherkassky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509061815
StatePublished - Jun 30 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Other2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States

Bibliographical note

Funding Information:
Acknowledgement: The work of V. Cherkassky was supported, in part, by NIH grant U01NS073557 and by NIH grant R01NS092882.

Publisher Copyright:
© 2017 IEEE.


  • Learning through contradiction
  • Support Vector Regression
  • Universum Learning

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