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基于集成最小二乘支持向量机方法的近红外光谱分析
引用本文:许剑良. 基于集成最小二乘支持向量机方法的近红外光谱分析[J]. 光谱实验室, 2011, 28(3): 1054-1057
作者姓名:许剑良
作者单位:浙江工商大学计算机与信息工程学院,杭州市下沙高教园区学正街18号,310018
摘    要:针对近红外(Near Infrared,NIR)光谱测量中的小样本问题。本文提出了一种集成最小二乘支持向量机(Ensemble Least Squares Support Vector Machine,ELS-SVM)新算法。首先使用随机子空间算法(Random Subspace Method,RSM)原始高维变量空间划分为若干个低维度的子空间,然后分别在各个子空间建立最小二乘支持向量机(LS-SVM)模型,最后构造一个集成结果来进行预测。针对一批柴油样本的实验结果表明,本法对柴油十六烷值的预测精度优于传统的LS-SVM方法。

关 键 词:集成学习  随机子空间算法  近红外光谱  最小二乘支持向量机

Near Infrared Spectral Analysis Based on Ensemble Least Squares Support Vector Machine Method
XU Jian-Liang. Near Infrared Spectral Analysis Based on Ensemble Least Squares Support Vector Machine Method[J]. Chinese Journal of Spectroscopy Laboratory, 2011, 28(3): 1054-1057
Authors:XU Jian-Liang
Affiliation:XU Jian-Liang(College of Computer and Information Engineering,Zhejiang Gongshang University,Hangzhou 310018,P.R.China)
Abstract:According to handling the small sample size(SSS) problem in near infrared(NIR) spectra measurement,an ensemble least squares support vector machines(ELS-SVM) approach was proposed.Firstly,the random subspace method(RSM) was introduced to divide the original high-dimensional space into some low-dimensional subspaces,then the LS-SVM prediction models were constructed on each subspace.Finally,an ensemble result was built for prediction.Applying the proposed approach to a set of diesel samples,the experimental ...
Keywords:Ensemble Learning  Random Subspace Method  Near Infrared Spectrum  LS-SVM  
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