elm kernel
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2019 ◽  
Vol 46 ◽  
pp. 173-192 ◽  
Author(s):  
Neelam Dabas ◽  
Ram Pal Singh


2015 ◽  
Vol 54 (1) ◽  
pp. 149-161 ◽  
Author(s):  
A. S. Muthanantha Murugavel ◽  
S. Ramakrishnan


2011 ◽  
Vol 128-129 ◽  
pp. 297-300
Author(s):  
Shao Wei Liu ◽  
Dong Yan ◽  
Zhi Hua Liu ◽  
Jian Tang

Spectral data such as near-infrared spectrum and frequency spectrum can simply the modeling of the difficulty-to-measured parameters. A novel modeling approach combined the feature extraction with extreme support vector regression (ESVR) is proposed. The latent variables space based feature extraction method can successfully complete the dimension reduction and independent variable extraction. The novel proposed ESVR leaning algorithm is realized by using extreme learning machine (ELM) kernel as SVR kernel, which is used to construct final models with better generalization. The experimental results based on the orange juice near-infrared spectra demonstrate that the proposed approach has better generalization performance and prediction accuracy.



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