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支持向量机的核方法研究及其在森林火灾视频识别中的应用.docx

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支持向量机的核方法研究及其在森林火灾视频识别中的应用 Abstract SupportVectorMachines(SVM)isamachinelearningalgorithmthathasbeenusedwidelyinvariousfieldssuchaspatternrecognition,textclassification,andimagerecognition.ThesuccessofSVMinmanyapplicationscanbeattributedtoitsexcellentperformanceinhandlingdatainhigh-dimensionalspaces.OneofthefeaturesthatcontributetotheexceptionalperformanceofSVMistheuseofkernelmethods.KernelmethodsofferapowerfultoolthatmakesSVMhighlyefficientinprocessingcomplexdata.Inthispaper,wewilldiscusstheuseofkernelmethodsinsupportvectormachinesanditsapplicationinforestfirevideorecognition. Introduction SupportVectorMachines(SVM)isawidelyusedmachinelearningalgorithmthatisbasedonusingdecisionboundariestoclassifydata.OneoftheprimaryreasonsforSVM'sefficacyinvariousapplicationsisitsabilitytohandledatainhigh-dimensionalspacesefficiently,thankstoitskernelmethods.Kernelmethodsprocessdatausingamappingtechniquethattransformsthedatapointsinalow-dimensionalspacetoahigher-dimensionalspace,whereasimplelinearclassifiercanthenclassifythedata.Inthispaper,wewillreviewthemostpopularkernelfunctionsanddiscusstheiradvantagesanddisadvantages.Additionally,wewillpresentanexampleofhowwecanapplySVMswithkernelmethodsinidentifyingforestfiresinvideodata. KernelmethodsinSupportVectorMachines Kernelmethodsaremathematicaltoolsusedtoapplyalinearclassifiertononlineardatainahigh-dimensionalspace.TheideabehindinstallingkernelmethodsinSVMsisthatthedecisionboundarycanbetransformedtoahigher-dimensionalspace,whichwillallowforasimplelinearclassifiertodividethedataintotwoormoreclasseseffectively.Theuseofkernelmethodstomapdecisionboundariestoahigher-dimensionalspaceisknownasthekerneltrick.InSVM,thekernelfunctionscanbeclassifiedintotwoprimarycategories:linearkernelfunctionsandnonlinearkernelfunctions. Linearkernelfunctionsarethesimplestkernelfunctions.Thelinearkernelfunctioncanbewrittenas: K(x,y)=xTy Wherexandyareinputvectors.Thelinearkernelfunctionisusedwhenthedataislinearlyseparable.Althoughthelinearkernelfunctionisless