Discrete Wavelet Transform-Support Vector Regression Model and Its Application in NIR Analysis of Corn
Support vector machine (SVM) has become more and more popular as method for learning from examples. The basic theory is well understood. SVM is based on the principle of structural risk minimization, which makes its generalization ability better than other traditional learning machine methods. Support vector regression (SVR) is based on SVM. In this paper, Discrete wavelet transform (DWT) combined with SVR was used in Near-infrared Spectroscopy analysis of corn. The purpose of this paper is to investigate the feasibility of DWT-SVM method in NIR analysis. Results suggest that the DWT-SVR model has better accuracy in forecast and higher computing speed than traditional methods.
support vector regression discrete wave transform near-infrared spectroscopy
Jiefang Liu Pumei Gao Liang Wu Yingfeng Zhao
Department of Mathematics, Henan Institute of Science and Technology Xinxiang 453003 China Xinke college, Henan Institute of Science and Technology Xinxiang 453003 China
国际会议
武汉
英文
890-892
2010-06-06（万方平台首次上网日期，不代表论文的发表时间）