Optimization-Based Extreme Learning Machine with Multi-kernel Learning Approach for Classification

Author(s):  
Le-le Cao ◽  
Wen-bing Huang ◽  
Fu-chun Sun
2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Chengzhang Zhu ◽  
Xinwang Liu ◽  
Qiang Liu ◽  
Yuewei Ming ◽  
Jianping Yin

We propose a distance based multiple kernel extreme learning machine (DBMK-ELM), which provides a two-stage multiple kernel learning approach with high efficiency. Specifically, DBMK-ELM first projects multiple kernels into a new space, in which new instances are reconstructed based on the distance of different sample labels. Subsequently, anl2-norm regularization least square, in which the normal vector corresponds to the kernel weights of a new kernel, is trained based on these new instances. After that, the new kernel is utilized to train and test extreme learning machine (ELM). Extensive experimental results demonstrate the superior performance of the proposed DBMK-ELM in terms of the accuracy and the computational cost.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 62458-62470
Author(s):  
Peipei Wang ◽  
Xinqi Zheng ◽  
Junhua Ku ◽  
Chunning Wang

2016 ◽  
Vol 45 (2) ◽  
pp. 703-725 ◽  
Author(s):  
Md. Zahangir Alom ◽  
Paheding Sidike ◽  
Tarek M. Taha ◽  
Vijayan K. Asari

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