预览加载中,请您耐心等待几秒...
1/2
2/2

在线预览结束,喜欢就下载吧,查找使用更方便

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

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