Name
Abstract | ||
|---|---|---|
Recently, SVMs are wildly applied to regression estimation, but the existing algorithms leave the choice of the kernel type and kernel parameters to the user. This is the main reason for regression performance degradation, especially for the complicated data even the nonlinear and non-stationary data. By introducing the ‘empirical mode decomposition (EMD)’ method, with which any complicated data set can be decomposed into a finite and often small number of ‘intrinsic mode functions’ (IMFs) based on the local characteristic time scale of the data, this paper propose an important extension to the SVM method: multi-scale support vector machine based on EMD, in which several kernels of different scales can be used simultaneously to approximate the target function in different scales. Experiment results demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11759966_151 | ISNN (1) |
Keywords | Field | DocType |
different scale,empirical mode decomposition,kernel parameter,regression estimation,intrinsic mode function,complicated data,regression performance degradation,kernel type,multi-scale support vector machine,svm method,non-stationary data,support vector machine | Kernel (linear algebra),Small number,Nonlinear system,Regression,Pattern recognition,Computer science,Regression analysis,Support vector machine,Artificial intelligence,Artificial neural network,Machine learning,Hilbert–Huang transform | Conference |
Volume | ISSN | ISBN |
3971 | 0302-9743 | 3-540-34439-X |
Citations | PageRank | References |
3 | 0.39 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Yang | 1 | 45 | 13.51 |
| Jun Guo | 2 | 1579 | 137.24 |
| Weiran Xu | 3 | 210 | 43.79 |
| Xiangfei Nie | 4 | 4 | 0.75 |
| Jian Wang | 5 | 302 | 48.27 |
| Jianjun Lei | 6 | 713 | 52.69 |