Compound Support Vector Machines Method with Application in Spctral Analysis
AN Xin1,SU Shi-guang,WANG Tao1,XU Shuo1,HUANG Wen-jiang2,ZHANG Lu-da1*
1. College of Science,China Agricultural University,Beijing 100094,China 2. Beijing Agriculture Information Technology Research Center,Beijing 100089,China
Abstract：Support vector classification (SVC) and support vector regression (SVR) are two main issues of support vector machines (SVM). The present paper combined the two issues,that is,first to built SVC model for classification,then to built SVR models for analysis,and thus brought forward compound support vector machines (CSVM). Based on an idea of simulation study,the CSVM algorithm was built and then validated by building a quantitative analysis model using high-spectrum and leaf nitrogen content data of 71 rice samples which were divided into modeling set and forecasting set randomly at the ratio of 51∶16. For 5 random experiments,the average correlation coefficient of predicted values and standard chemical ones by Kjeldahl’s method of leaf nitrogen content was 0.89,and the average absolute error was 0.088,of which the corresponding values produced by traditional method were 0.87 and 0.091 respectively. It was concluded that the prediction precision of CSVM is higher than that of traditional SVM. CSVM provides a new idea for chemometrics quantitative analysis.
Key words：Compound support vector machines(CSVM);Hyperspectrum;Regression model;Leaf nitrogen content
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