Name
Abstract | ||
|---|---|---|
The classical ridge regression technique makes an assumption that the noise is Gaussian. However, it is reported that the noise models in some practical applications do not satisfy Gaussian distribution, such as wind speed prediction. In this case, the classical regression techniques are not optimal. So we derive an optimal loss function and construct a new framework of kernel ridge regression technique for general noise model (N-KRR). The Augmented Lagrangian Multiplier method is introduced to solve N-KRR. We test the proposed technique on artificial data and short-term wind speed prediction. Experimental results confirm the effectiveness of the proposed model. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.07.051 | Neurocomputing |
Keywords | Field | DocType |
Kernel ridge regression,Noise model,Loss function,Equality constraints,Short-term wind speed prediction | Kernel (linear algebra),Wind speed,Principal component regression,Regression,Ridge,Kernel ridge regression,Gaussian,Augmented Lagrangian method,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
149 | 0925-2312 | 5 |
PageRank | References | Authors |
0.45 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiguang Zhang | 1 | 42 | 7.21 |
| Qinghua Hu | 2 | 4028 | 171.50 |
| Zongxia Xie | 3 | 683 | 23.38 |
| Ju-Sheng Mi | 4 | 2054 | 77.81 |