Abstract
In this research, a robust optimization approach applied to support vector regression (SVR) is investigated. A novel kernel based-method is developed to address the problem of data uncertainty where each data point is inside a sphere. The model is called robust SVR. Computational results show that the resulting robust SVR model is better than traditional SVR in terms of robustness and generalization error.
Original language | English |
---|---|
Pages (from-to) | 424-429 |
Number of pages | 6 |
Journal | WSEAS Transactions on Systems |
Volume | 5 |
Issue number | 2 |
Publication status | Published - Feb 2006 |
Keywords
- Kernel Method
- Regression
- Robust Optimization
- Support Vector Machine
- Uncertainty