Universum Learning for SVM Regression
| Autor: | Dhar, Sauptik, Cherkassky, Vladimir |
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| Rok vydání: | 2016 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 10 pages,11 figures, Thesis: http://conservancy.umn.edu/handle/11299/162636 |
| Databáze: | arXiv |
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