预览加载中，请您耐心等待几秒...

支持向量机及在近红外光谱分析中的应用(英文).docx

预览
1/2
2/2

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

支持向量机及在近红外光谱分析中的应用(英文) SupportVectorMachines(SVMs)andTheirApplicationinNear-InfraredSpectroscopyAnalysis Introduction Near-infraredspectroscopy(NIR)isawidelyusedanalyticaltechniqueinthefieldofchemicalanalysis.Ithasbecomeincreasinglyimportantasatoolfornondestructiveandfastanalysisofsamplesinthepharmaceuticalindustry,foodindustry,andseveralotherfields.However,thecomplexityofNIRspectradatamakesitchallengingtoconductclassificationandregressionanalyses,whichiscrucialfordetectingandidentifyingvariouscomponentsinthesample.Asolutiontothisproblemisusingsupportvectormachines(SVMs),whichhaveshownremarkableperformanceinNIRanalysis.Inthispaper,wewilldiscusstheprinciplesofSVMsandtheirapplicationinNIRanalysis. PrinciplesofSVMs SVMsaresupervisedlearningmodelsthatarecommonlyusedforclassificationandregressiontasks.Givenasetofinputobservations,theSVMalgorithmfindsthebestdecisionboundarythatseparatestheclassesinthefeaturespace.Thebestboundaryisdeterminedbymaximizingthemarginbetweentheclasses,whichisthedistancebetweenthedecisionboundaryandtheclosestdatapoints.TheSVMalgorithmachievesthisbytransformingthefeaturespaceintoahigherdimension.ThistransformationallowstheSVMtofindtheoptimalseparatingplanethatmaximizesthemargininthetransformedspace. OneofthekeyfeaturesofSVMsisthattheyonlyuseasubsetofthedatapointscalledsupportvectorstodefinethedecisionboundary.ThesupportvectorsarethedatapointsclosesttothedecisionboundaryandplayacriticalroleintheoptimizationoftheSVMalgorithm.ThisfeaturemakesSVMscomputationallyefficientandrobusttooutliersinthedata. ApplicationofSVMsinNIRAnalysis SVMshavebeenwidelyusedforclassificationandregressionanalysisinNIRspectroscopy.Classificationtasksinvolveseparatingsamplesintodifferentclassesbasedontheirspectralfeatures.Regressiontasks,ontheotherhand,involvepredictingacontinuousoutputvariablefromthespectraldata.ThefollowingaresomeexamplesofhowSVMshavebeenusedinNIRanalysis: 1.ClassificationofCrops:SVMshavebeenusedtoclassifygrains,vegetables,andfruitsbasedontheirNIRspectra.The