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 language||English (US)|
|Title of host publication||2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Jun 30 2017|
|Event||2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States|
Duration: May 14 2017 → May 19 2017
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Other||2017 International Joint Conference on Neural Networks, IJCNN 2017|
|Period||5/14/17 → 5/19/17|
Bibliographical noteFunding Information:
Acknowledgement: The work of V. Cherkassky was supported, in part, by NIH grant U01NS073557 and by NIH grant R01NS092882.
© 2017 IEEE.
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- Support Vector Regression
- Universum Learning