Kernel Extreme Learning Machine Based on <i>K</i> Interpolation Simplex Method

Yidan SU; Ruoyu LI; Hua QIN; Qin CHEN

doi:10.11999/JEIT171104

Volume 40 Issue 8

Aug. 2018

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Yidan SU, Ruoyu LI, Hua QIN, Qin CHEN. Kernel Extreme Learning Machine Based on K Interpolation Simplex Method[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1860-1866. doi: 10.11999/JEIT171104

Citation:

Yidan SU, Ruoyu LI, Hua QIN, Qin CHEN. Kernel Extreme Learning Machine Based on K Interpolation Simplex Method[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1860-1866. doi: 10.11999/JEIT171104

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Kernel Extreme Learning Machine Based on K Interpolation Simplex Method

doi: 10.11999/JEIT171104

College of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Funds: The National Natural Science Foundation of China (61762009)

Received Date: 2017-11-24
Rev Recd Date: 2018-04-23

Available Online: 2018-06-07

Publish Date: 2018-08-01

Abstract

Abstract

The kernel Extreme Learning Machine (ELM) has a problem that the kernel parameter of the Gauss kernel function is hard to be optimized. As a result, training speed and classification accuracy of kernel ELM are negatively affected. To deal with that problem, a novel kernel ELM based on K interpolation simplex method is proposed. The training process of kernel ELM is considered as an unconstrained optimal problem. Then, the Nelder-Mead Simplex Method (NMSM) is used as an optimal method to search the optimized kernel parameter, which improves the classification accuracy of kernel ELM. Furthermore, the K interpolation method is used to provide appropriate initial values for the Nelder-Mead simplex to reduce the number of iterations, and as a result, the training speed of ELM is improved. Comparative results on UCI dataset demonstrate that the novel ELM algorithm has better training speed and higher classification accuracy.
- Kernel Extreme Learning Machine (KELM),
- Kernel parameter,
- Nelder-Mead Simplex Method (NMSM),
- K Interpolation method

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References

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