Paper Title:
Spectral Data Modeling Based on Feature Extraction and Extreme Support Vector Regression
  Abstract

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
Chapter
Chapter 1: Mechanic Manufacturing System and Automation
Edited by
Zhixiang Hou
Pages
297-300
DOI
10.4028/www.scientific.net/AMM.128-129.297
Citation
S. W. Liu, D. Yan, Z. H. Liu, J. Tang, "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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$32.00
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