Name
Abstract | ||
|---|---|---|
The climate model is the crucial factor for agriculture. However, the climate variables, which were strongly corrupted by noises or fluctuations, are complicated process and can not be reconstructed by a common method. In the paper, we adapt the SVM to predict it. Specifically, we incorporate the initial condition on climate variables to the training of SVM. The numerical results show the effectiveness and efficiency of the approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FSKD.2009.566 | FSKD |
Keywords | Field | DocType |
climate mitigation,svm training,initial condition,common method,climate model,crucial factor,initial conditions,climate prediction,svm,numerical result,agriculture,support vector machine,climate prediction model,climate variable,complicated process,support vector machines,data mining,correlation,meteorology,optimization,kernel | Kernel (linear algebra),Data mining,Climate model,Computer science,Support vector machine,Artificial intelligence,Initial value problem,Machine learning | Conference |
Volume | ISBN | Citations |
5 | 978-0-7695-3735-1 | 1 |
PageRank | References | Authors |
0.40 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deji Wang | 1 | 1 | 0.73 |
| Xu Bo | 2 | 4 | 0.90 |
| Faquan Zhang | 3 | 1 | 0.73 |
| Jianting Li | 4 | 2 | 1.22 |
| Li Guangcai | 5 | 1 | 0.40 |
| Bingyu Sun | 6 | 358 | 23.31 |