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《Journal of Jilin University(Earth Science Edition)》 2006-04
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Evaluation of Lake Water Quality Based on Classification Algorithms of Support Vector Machines

XU Hong-min~(1,2),YANG Tian-xing~21.Department of Mathematics and Physics,Beijing Institute of Petrochemical Technology,Beijing 102617,China2.College of GeoExploration Science and Technology,Jilin University,Changchun 130026,China  
Support vector machines(SVM) were developed from the machine learning theory of small samples based on statistical learning theory(SLT) by Vapnik et al,which were originally designed for binary classification problems.It can solve small-sample learning problems better by using structural risk minimization in place of experiential risk minimization.Moreover,SVM can convert a nonlinear learning problem into a linear learning problem in order to reduce the algorithm complexity by using the kernel function concept.A multi-class classification method of SVM is applied to lake water quality assessment.A case study shows that the method is reliable in the classification and evaluation of lake water quality.
【Fund】: 国家“973”项目(G1999045705)
【CateGory Index】: X824
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