Abstract
The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM. Combined the LS-SVM with the Multi-Resolution Analysis (MRA), this letter proposes the Multi-resolution LS-SVM (MLS-SVM). The proposed algorithm has the same theoretical framework as MRA but with better approximation ability. At a fixed scale MLS-SVM is a classical LS-SVM, but MLS-SVM can gradually approximate the target function at different scales. In experiments, the MLS-SVM is used for nonlinear system identification, and achieves better identification accuracy.
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Communication author: Wang Liejun, born in 1976, male, doctor candidate. Dept of Information and Communication Engineering, Xi’an Jiaotong University, Xi’an 710049, China.
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Wang, L., Zhang, T. & Zhou, Y. Multi-Resolution Least Squares Support Vector Machines. J. of Electron.(China) 24, 701–704 (2007). https://doi.org/10.1007/s11767-006-0270-7
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DOI: https://doi.org/10.1007/s11767-006-0270-7
Key words
- Support Vector Machines (SVM)
- Least square method
- Multi-Resolution Analysis (MRA)
- Nonlinear system identification