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页数:15页
时间:2019-07-31
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1、Author'spersonalcopyCognComput(2014)6:376–390DOI10.1007/s12559-014-9255-2AnInsightintoExtremeLearningMachines:RandomNeurons,RandomFeaturesandKernelsGuang-BinHuangReceived:25January2014/Accepted:13March2014/Publishedonline:3April2014ÓSpringerScience+BusinessMediaNewYork2014AbstractExtremelearningmach
2、ines(ELMs)basicallyroleinbothresearchandclassificationrelatedapplicationsgiveanswerstotwofundamentallearningproblems:(1)inthepasttwodecades.SVMachieveshighergeneral-Canfundamentalsoflearning(i.e.,featurelearning,clus-izationperformancethanconventionalartificialneuraltering,regressionandclassification)b
3、emadewithoutnetworksinmostclassificationapplications.Intheoriginaltuninghiddenneurons(includingbiologicalneurons)evenimplementationofSVM,onehastohandleaquadraticwhentheoutputshapesandfunctionmodelingoftheseprogramming(QP)problemwhichisusuallytediousandneuronsareunknown?(2)Doesthereexistunifiedframe-ti
4、meconsuming.AsoneofthemainvariantofSVM,Leastworkforfeedforwardneuralnetworksandfeaturespacesquaresupportvectormachine(LS-SVM)[2]aimstoavoidmethods?ELMsthathavebuiltsometangiblelinkstheQPproblemusingequalityconstraintsinsteadofthebetweenmachinelearningtechniquesandbiologicalinequalityconstraintadopte
5、dinconventionalSVM.Com-learningmechanismshaverecentlyattractedincreasingparedwithSVM,LS-SVMiseaseofimplementation.attentionofresearchersinwidespreadresearchareas.ThisExtremelearningmachines(ELMs)[3–7]becomeattrac-paperprovidesaninsightintoELMsinthreeaspects,viz:tivetomoreandmoreresearchersrecently[8
6、–20].Thisrandomneurons,randomfeaturesandkernels.ThispaperpaperaimstoreviewtheELMfromrandomneuronsandalsoshowsthatintheoryELMs(withthesamekernels)kernelspointofviewandtobuildsomerelationshipandtendtooutperformsupportvectormachineanditsvariantslinksbetweenELM,SVMandotherrelatedmachineinbothregressiona
7、ndclassificationapplicationswithlearningtechniques.Althoughitisoutofquestionthatmucheasierimplementation.SVManditsvariantsachievesurprisingperformanceinmostapplications,differentfromsomecommonconceptin
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