Spectral Data Modeling Based on Feature Extraction and Extreme Support Vector Regression

Abstract:

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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.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

297-300

DOI:

10.4028/www.scientific.net/AMM.128-129.297

Citation:

S. W. Liu et al., "Spectral Data Modeling Based on Feature Extraction and Extreme Support Vector Regression", Applied Mechanics and Materials, Vols. 128-129, pp. 297-300, 2012

Online since:

October 2011

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Price:

$35.00

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