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基于遗传算法优化支持向量机的图像识别
Title:OptimizationofSupportVectorMachinesforImageRecognitionusingGeneticAlgorithms
Abstract:
SupportVectorMachines(SVMs)havebeenwidelyusedforimagerecognitiontasksduetotheirabilitytohandlehigh-dimensionaldataandadapttononlineardecisionboundaries.However,findingtheoptimalsetofparametersforSVMsisachallengingtask.Thispaperintroducesanapproachtooptimizesupportvectormachinesusinggeneticalgorithmsforimagerecognitiontasks.TheproposedmethodaimstoimprovetheclassificationperformancebysearchingfortheoptimalcombinationofSVMhyperparameters.Theeffectivenessoftheapproachisdemonstratedthroughexperimentalresultsonvariousimagedatasets,showingsignificantimprovementsovertraditionalSVMmodels.
1.Introduction
Imagerecognitionisanimportantareaincomputervisionandpatternrecognition,withapplicationsrangingfromobjectdetectionandscenerecognitiontomedicalimageanalysis.SupportVectorMachines(SVMs)haveproventobeeffectiveinsolvingavarietyofclassificationproblems,includingimagerecognition.However,selectingtheappropriatehyperparametersoftheSVMiscrucialforachievinggoodclassificationperformance.Geneticalgorithmsprovideapowerfuloptimizationtechniquethatcaneffectivelysearchthehigh-dimensionalparameterspaceofSVMs.
2.SupportVectorMachines
Thissectionprovidesabriefoverviewofsupportvectormachines,includingtheirformulationandthevariouskernelfunctionsusedfornonlinearclassification.TheadvantagesandlimitationsofSVMsinimagerecognitionarealsodiscussed.
3.GeneticAlgorithms
Geneticalgorithmsareevolutionarysearchalgorithmsinspiredbytheprocessofnaturalselection.Theyuseapopulationofcandidatesolutions,whereeachsolutionrepresentsasetofSVMhyperparameters.Thegeneticalgorithmiterativelyevolvesthepopulationbyapplyinggeneticoperatorssuchasselection,crossover,andmutation.ThefitnessofeachcandidatesolutionisevaluatedbasedontheSVM'sclassificationperformanceonavalidationdataset.
4.OptimizationofSVMsusingGeneticAlgorithms
ThissectionpresentstheproposedmethodforoptimizingSVMsusinggeneticalgorithmsforimagerecognition