基于M估计器的支持向量机算法及其应用  

M-estimator based support vector machine and its application

作　　者：包鑫[1] 戴连奎[1] 

机构地区：[1]浙江大学工业控制技术国家重点实验室,浙江杭州310027

出　　处：《化工学报》2009年第7期1739-1745,共7页CIESC Journal

基　　金：国家高技术研究发展计划项目(2006AA04Z169)~~

摘　　要：训练样本的准确性对回归分析模型有很大的影响,然而训练样本中难免会出现一些造成分析模型失效的奇异点。为克服奇异点对回归模型的影响,本文提出了一种基于M估计器的支持向量机(M-SVM)。它采用M估计器的目标函数代替最小二乘支持向量机(LS-SVM)目标函数中的残差平方和,同时提出了M-SVM的迭代求解算法,并将该算法应用于含有奇异点的低维仿真数据回归和汽油近红外光谱定量分析中。实验结果证明,相比于其他的支持向量机,M-SVM具有更好的稳健性和分析精度。Validity of training samples is important to regression model, however, outliers in training sample sets are hard to avoid, which will disturb the model accuracy. In order to overcome the influence of outliers to regression model, a new M-estimator based support vector machine （M-SVM） was introduced. It replaced the sum of square residuals in least squares support vector machine （LS-SVM） with objective function of M-estimator. An iterative modeling algorithm of the M-SVM was proposed. The M-SVM was used in a low-dimension data regression problem with outliers and in the NIR spectral analysis of gasoline research octane number. Experimental results showed that the M-SVM was more robust and accurate than other SVMs.

关 键 词：M估计器 最小二乘支持向量机 稳健建模 光谱分析 

分 类 号：TQ018[化学工程] TP274[自动化与计算机技术—检测技术与自动化装置]