Name
Abstract | ||
|---|---|---|
Based on the spectral data from SDSS, Kernel Support Vector Machines (K-SVM) is applied to classify quasars from other celestial body. Firstly, the basic theory of the SVM(Support Vector Machine) with relaxation factor and kernel function is introduced. Then, the main parameters are designed and selected. Finally, the method is applied to the classification and identification of the quasars. The classification results of different kernel functions and different parameters are compared and verified. The experimental results show that the accuracy is improved by the proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICInfA.2017.8078935 | 2017 IEEE International Conference on Information and Automation (ICIA) |
Keywords | Field | DocType |
Quasar,SVM,Classification,Kernel function | Least squares support vector machine,Pattern recognition,Radial basis function kernel,Computer science,Kernel embedding of distributions,Support vector machine,Polynomial kernel,Artificial intelligence,Relevance vector machine,Kernel method,Kernel (statistics) | Conference |
ISBN | Citations | PageRank |
978-1-5386-3155-3 | 0 | 0.34 |
References | Authors | |
2 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| li zhang | 1 | 101 | 18.22 |
| Zhang Chenjin | 2 | 0 | 0.34 |
| Qingyang Xu | 3 | 10 | 5.02 |
| Su Yanrui | 4 | 0 | 0.34 |
| Zhang Yunsi | 5 | 0 | 0.34 |
| Chengyong Liu | 6 | 0 | 0.34 |
| Fang Liu | 7 | 1188 | 125.46 |
| Bao Zengjun | 8 | 0 | 0.34 |