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MLP高阶重构格式应用 Title:ApplicationofMLPHigher-orderRefactoringTechniques Abstract: TheMultilayerPerceptron(MLP)isapopulartypeofartificialneuralnetwork,widelyemployedinvariousapplications,includingclassification,forecasting,andpatternrecognition.Overtime,researchersandpractitionershavefocusedonenhancingtheperformanceofMLPsthroughvarioustechniques,oneofwhichishigher-orderrefactoring.Inthispaper,wediscusstheapplicationofMLPhigher-orderrefactoringtechniquesinthecontextofimprovingclassificationaccuracyinareal-worlddataset. Introduction: MLPisafeedforwardneuralnetworkmodelconsistingofmultiplelayersofnodesorneurons.Eachnodeinalayerperformsaweightedsummationofinputsandpassesthemthroughanactivationfunctiontoproduceanoutput.MLPshavebeensuccessfulinawiderangeoftasksduetotheirabilitytolearncomplexmappingsbetweeninputandoutputdata.However,achievinghigheraccuracyinclassificationtasksisalwaysachallenge.Therefore,theexplorationofhigher-orderrefactoringtechniquesbecomescrucialinimprovingMLPperformance. Methodology: Thehigher-orderrefactoringtechniquesfocusonmodifyingthestructureandtrainingofMLPstooptimizetheirperformance.Inthisstudy,weconsiderthreekeytechniques:(1)Dropout,(2)BatchNormalization,and(3)Regularization. 1.Dropout: Dropoutisapowerfultechniquethatpreventsoverfittingbyrandomlydisablingafractionoftheneuronsduringtraining.Thisallowsdifferentsubsetsofneuronstolearnandadapttothedata,leadingtoamorerobustmodel.Byreducinginterdependencebetweenneurons,dropoutenhancestheaccuracyoftheMLPbypreventingoverrelianceonspecificfeatures. 2.BatchNormalization: BatchNormalizationisatechniquethatnormalizestheactivationsofeachlayerwithintheMLP.Ithelpsinreducingtheinternalcovariateshift,wherethedistributionofinputschangesoverthecourseoftraining.Bynormalizingtheinput,theMLPcanbetterlearnandadapttothedata,leadingtofasterconvergenceandimprovedaccuracy. 3.Regularization: Regularizationtechniques,suchasL1andL2regularization,playacrucialroleinpreventingoverfittingandincreasingthegeneralizationabilityofMLPs.

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