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CWT-Support Vector Regression Model and Its application

机译:CWT-Support Vector回归模型及其应用

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摘要

Near-infrared spectroscopy (NIR) analytical technique is simple,fast and low cost,making neither pollution nor damage to the samples,and can determine many components simultaneously.Continuous wavelet transform (CWT),as an application direction of the wavelet analysis,is keener to the signal slight change.Support vector machine (SVM) is based on the principle of structural risk minimization,which makes SVM has better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization.In this paper,we use CWT- SVM model to predict meat's component.Compared with Partial Least Squares (PLS) and SVR,we get more satisfactory result.
机译:近红外光谱(NIR)分析技术简便,快速,成本低廉,对样品无污染也无损害,可同时测定多种成分。连续小波变换(CWT)作为小波分析的应用方向支持向量机(SVM)基于结构风险最小化原理,这使得SVM具有比其他基于经验风险最小化学习原理的传统学习机更好的泛化能力。 ,我们使用CWT-SVM模型预测肉的成分。与偏最小二乘(PLS)和SVR相比,我们得到了更满意的结果。

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