Discrete Wavelet Transform-Support Vector Regression Model and Its Application in NIR Analysis of Corn

机译：离散小波变换 - 支持向量回归模型及其在玉米NIR分析中的应用

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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 it's 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.

机译：支持向量机（SVM）作为从示例学习的方法变得越来越受欢迎。基本理论得到了很好的理解。 SVM基于结构风险最小化的原则，这使得它的泛化能力优于其他传统学习机方法。支持向量回归（SVR）基于SVM。本文使用离散小波变换（DWT）与SVR结合使用，用于玉米的近红外光谱分析。本文的目的是探讨DWT-SVM方法在NIR分析中的可行性。结果表明，DWT-SVR模型在预测中具有更好的准确性和比传统方法更高的计算速度。

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《International conference on information technology and industrial engineering》|2010年||共3页
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Jiefang Liu; Pumei Gao; Liang Wu; Yingfeng Zhao;
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正文语种
中图分类信息与知识传播;
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support vector regression; discrete wave transform; near-infrared spectroscopy;

机译：支持向量回归;离散波变换;近红外光谱;

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4. Discrete Wavelet Transform-Support Vector Regression Model and Its Application in NIR Analysis of Corn [C] . Jiefang Liu, Pumei Gao, Liang Wu, ITIE 2010;International conference on information technology and industrial engineering . 2010

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Discrete Wavelet Transform-Support Vector Regression Model and Its Application in NIR Analysis of Corn

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