知识推送系统中一种基于多分类径向基神经网络的知识匹配方法（英文）-豆柴文库

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知识推送系统中一种基于多分类径向基神经网络的知识匹配方法（英文）
Introduction
Theadvancementintechnologyhasledtoanoverwhelmingamountofdataavailableinvariousdomains.Withthisincreaseindata,ithasbecomechallengingtolocateandmatchthemostrelevantandaccurateinformationtotheuser'ssearchquery.Thisproblemisspecificallyprevalentinknowledge-basedsystems,wherethepromptnessandprecisionofthesearchresultsdeterminethesystem'seffectiveness.Therefore,thereisadireneedforefficientandeffectiveknowledgematchingtechniquesinthesesystems.
Inthispaper,weproposeaknowledgematchingmethodbasedonamulti-classradialbasisfunctionneuralnetwork.Ourapproachnotonlyprovidesanefficientandaccuratematchingmethod,butitalsoreducesthememoryusageandcomputationalcostbyutilizingtheRBFneuralnetwork'sabilitytogeneralizewellonunseendata.
Methodology
Ourproposedknowledgematchingmethodincludesthefollowingsteps:
1.Pre-processing:Inthisstep,weapplypre-processingtechniquestotheinputdata,suchastextnormalization,tokenization,stop-wordremoval,andstemming.Pre-processingeliminatesanyirrelevantorredundantinformationandstandardizestheinputdatainastructuredformat.
2.FeatureExtraction:Thepre-processeddataisthentransformedintofeaturevectorsusingthebag-of-wordsmodel.Thebag-of-wordsmodelrepresentseachinputdatainstanceasavectorofwordfrequencies.ThefeaturevectorsareusedasinputtotheRBFneuralnetwork.
3.TrainingRBFNeuralNetwork:Weuseamulti-classRBFneuralnetworkforknowledgematching.Themulti-classclassificationneuralnetworkconsistsofmultipleoutputvectorswithonevectorforeachclass.Thenetwork'strainingisdoneusingthebackpropagationalgorithm,wherethenetwork'sconnectionweightsareadjustedtominimizetheclassificationerror.Wethenvalidatethetrainednetworkonaheld-outdatasettoconfirmitsaccuracyandgeneralizability.
4.KnowledgeMatching:OncetheRBFneuralnetworkhasbeentrainedandvalidated,weuseitforknowledgematching.Whenauserqueryisinputtedtothesystem,itispre-processed,andafeaturevectoriscreated.ThisvectoristhenfedtothetrainedRBFneuralnetwork,whichpredictsthecorrectclass(orknowledge)corresp