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基于MapReduce的非线性支持向量机分类算法研究.docx

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基于MapReduce的非线性支持向量机分类算法研究 Abstract: SupportVectorMachines(SVMs)havebeenwidelyusedinclassificationduetotheirexcellentclassificationperformance.However,traditionalSVMshavetheproblemofhighcomputationalcomplexitywhendealingwithalargeamountofdata.Tosolvethisproblem,theMapReduce-basedSVMalgorithmhasbeenproposed,whichcaneffectivelyimprovethecomputationalefficiencyofSVMsinlargedataenvironments.Inthispaper,wefocusontheresearchofMapReduce-basednon-linearSVMclassificationalgorithms. 1.Introduction SupportVectorMachines(SVMs)havebeenwidelyusedinmachinelearning,especiallyinclassificationproblems.SVMsarecharacterizedbytheirexcellentgeneralizationabilityandhighclassificationaccuracy.SVMsachieveoptimalclassificationbyfindingthebesthyperplanethatmaximallyseparatesthetwoclasses.ThetraditionalSVMalgorithmhasthecharacteristicsofhighclassificationaccuracy,butwhenitfaceslarge-scaledata,itscomputationalcomplexityisveryhigh,whichlimitsitsapplicationinsomescenarios.Tosolvethisproblem,MapReduce-basedSVMalgorithmshavebeenproposed,whichcaneffectivelyimprovethecomputationalefficiencyofSVMsinlarge-scaledataenvironments. 2.OverviewofMapReduce MapReduceisadistributedcomputingframeworkthathasbeenwidelyusedinbigdataprocessing.TheMapReduceframeworkprocessesbigdatainparallelonmultiplenodes,whichgreatlyimprovesthecomputationalefficiencyofbigdataprocessing.TheMapReduceframeworkconsistsoftwophases:MapandReduce.IntheMapphase,theinputdataisdividedintomultiplesubtasksandisprocessedinparallelonmultiplenodes.IntheReducephase,theresultsoftheMapphasearethenshuffledandsorted,andthefinaloutputisgenerated. 3.MapReduce-basedSVMAlgorithm TheMapReduce-basedSVMalgorithmcanefficientlyprocesslarge-scaledatabydividingthedataintosubsetsandprocessingtheminparallel.TheMapReduce-basedSVMalgorithmconsistsofthefollowingsteps: (1)Datapreprocessing Inthisstep,theinputdataispreprocessedtoprepareitfortheSVMalgorithm.Thedataisnormalizedandfeaturescalingisperformed. (2)Dividedataintosubsets Thelarge-scaleinputdataisdividedintomulti