Name
Abstract | ||
|---|---|---|
In the process of the roadbed disease recognition, as a result of the complexity and immaturity of the roadbed medium, the ground penetrating radar signal explanation has multi-solutions. It affects the application of ground penetrating radar and the roadbed disease detection. The support vector machines is one algorithm based on the structural risk minimization principle, it can obtain the overall optimal solution in sample less situations. It has solved the inevitable partial minimum problem and overcome the disadvantage, which the traditional neural network cannot avoid. In this paper the ground penetrating radar signal explanation model is established and applied in recognition to the roadbed disease using the support vector machine theory and methods. The result has proved that this method may enhance ground penetrating radar signal explanation precision and efficiency. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74769-7_57 | LSMS (1) |
Keywords | Field | DocType |
radar signal explanation model,support vector machine,roadbed medium,roadbed disease,radar signal explanation,support vector machine theory,inevitable partial minimum problem,roadbed disease recognition,radar signal explanation precision,signal interpretation,roadbed disease detection,structural risk minimization,neural network,ground penetrating radar | Ground-penetrating radar,Support vector machine,Algorithm,Artificial intelligence,Structural risk minimization,Engineering,Artificial neural network,Machine learning,Kernel (statistics) | Conference |
Volume | ISSN | ISBN |
4688 | 0302-9743 | 3-540-74768-0 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huasheng Zou | 1 | 0 | 0.34 |
| Feng Yang | 2 | 18 | 4.42 |