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外文翻译--机器学习的研究.doc

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外文翻译--机器学习的研究.doc

1MACHINELEARNINGRESEARCHFOURCURRENTDIRECTIONSTHOMASGDIETTERICH■MACHINELEARNINGRESEARCHHASBEENMAKINGGREATPROGRESSINMANYDIRECTIONSTHISARTICLESUMMARIZESFOUROFTHESEDIRECTIONSANDDISCUSSESSOMECURRENTOPENPROBLEMSTHEFOURDIRECTIONSARE1THEIMPROVEMENTOFCLASSIFICATIONACCURACYBYLEARNINGENSEMBLESOFCLASSIFIERS,2METHODSFORSCALINGUPSUPERVISEDLEARNINGALGORITHMS,3REINFORCEMENTLEARNING,AND4THELEARNINGOFCOMPLEXSTOCHASTICMODELSTHELASTFIVEYEARSHAVESEENANEXPLOSIONINMACHINELEARNINGRESEARCHTHISEXPLOSIONHASMANYCAUSESFIRST,SEPARATERESEARCHCOMMUNITIESINSYMBOLICMACHINELEARNING,COMPUTATIONLEARNINGTHEORY,NEURALNETWORKS,STATISTICS,ANDPATTERNRECOGNITIONHAVEDISCOVEREDONEANOTHERANDBEGUNTOWORKTOGETHERSECOND,MACHINELEARNINGTECHNIQUESAREBEINGAPPLIEDTONEWKINDSOFPROBLEM,INCLUDINGKNOWLEDGEDISCOVERYINDATABASES,LANGUAGEPROCESSING,ROBOTCONTROL,ANDCOMBINATORIALOPTIMIZATION,ASWELLASTOMORETRADITIONALPROBLEMSSUCHASSPEECHRECOGNITION,FACERECOGNITION,HANDWRITINGRECOGNITION,MEDICALDATAANALYSIS,ANDGAMEPLAYINGINTHISARTICLE,ISELECTEDFOURTOPICSWITHINMACHINELEARNINGWHERETHEREHASBEENALOTOFRECENTACTIVITYTHEPURPOSEOFTHEARTICLEISTODESCRIBETHERESULTSINTHESEAREASTOABROADERAIAUDIENCEANDTOSKETCHSOMEOFTHEOPENRESEARCHPROBLEMSTHETOPICAREASARE1ENSEMBLESOFCLASSIFIERS,2METHODSFORSCALINGUPSUPERVISEDLEARNINGALGORITHMS,3REINFORCEMENTLEARNING,AND4THELEARNINGOFCOMPLEXSTOCHASTICMODELSTHEREADERSHOULDBECAUTIONEDTHATTHISARTICLEISNOTACOMPREHENSIVEREVIEWOFEACHOFTHESETOPICSRATHER,MYGOALISTOPROVIDEAREPRESENTATIVESAMPLEOFTHERESEARCHINEACHOFTHESEFOURAREASINEACHOFTHEAREAS,THEREAREMANYOTHERPAPERSTHATDESCRIBERELEVANTWORKIAPOLOGIZETOTHOSEAUTHORSWHOSEWORKIWASUNABLETOINCLUDEINTHEARTICLEENSEMBLESOFCLASSIFIERSTHEFIRSTTOPICCONCERNSMETHODSFORIMPROVINGACCURACYINSUPERVISEDLEARNINGIBEGINBYINTRODUCINGSOMENOTATIONINSUPERVISEDLEARNING,ALEARNINGPROGRAMISGIVENTRAININGEXAMPLESOFTHEFORM{X1,Y1,,XM,YM}FORSOMEUNKNOWNFUNCTIONYFXTHEXIVALUESARETYPICALLYVECTORSOFTHEFORMXI,1,XI,2,,XI,NWHOSECOMPONENTSAREDISCRETEORREALVALUED,SUCHASHEIGHT,WEIGHT,COLOR,ANDAGETHESEAREALSOCALLEDTHEFEATUREOFXI,IUSETHENOTATIONXIJTOREFERTO2THEJTHFEATUREOFXIINSOMESITUATIONS,IDROPTHEISUBSCRIPTWHENITISIMPLIEDBYTHECONTEXTTHEYVALUESARETYPICALLYDRAWNFROMADISCRETESETOFCLASSES{1,,K}INTHECASEOFCLASSIFICATIONORFROMTHEREALLINEINTHECASEOFREGRESSIONINTHISARTICLE,IFOCUSPRIMARILYONCLASSIFICATIONTHETRAININGEXAMPLESMIGHTBECORRUPTEDBYSOMERANDOMNOISEGIVENASETSOFTRAININGEXAMPLES,ALEARNINGALGORITHMOUTPUTSACLASSIFIERTHECLASSIFIERISAHYPOTHESISABOUTTHETRUEFUNCTIONFGIVENNEWXVALUES,ITPREDICTSTHECORRESPONDINGYVALUESIDENOTECLASSIFIERSBYH1,,HIANENSEMBLEOFCLASSIFIERISASETOFCLASSIFIERSWHOSEINDIVIDUALDECISIONSARECOMBINEDINSOMEWAYTYPICALLYBYWEIGHTEDORUNWEIGHTEDVOTINGTOCLASSIFYNEWEXAMPLESONEOFTHEMOSTACTIVEAREASOFRESEARCHINSUPERVISEDLEARNINGHASBEENTHESTUDYOFMETHODSFORCONSTRUCTINGGOODENSEMBLESOFCLASSIFIERSTHEMAINDISCOVERYISTHATENSEMBLESAREOFTENMUCHMOREACCURATETHANTHEINDIVIDUALCLASSIFIERSTHATMAKETHEMUPANENSEMBLECANBEEMOREACCURATETHANITSCOMPONENTCLASSIFIERSONLYIFTHEINDIVIDUALCLASSIFIERSDISAGREEWITHONEANOTHERHANSENANDSALAMON1990TOSEEWHY,IMAGINETHATWEHAVEANENSEMBLEOFTHREECLASSIFIERS{H1,H2,H3},ANDCONSIDERANEWCASEXIFTHETHREECLASSIFIERSAREIDENTICAL,THENWHENH1XISWRONG,H2XANDH3XAREALSOWRONGHOWEVER,IFTHEERRORSMADEBYTHECLASSIFIERSAREUNCORRELATED,THENWHENH1XISWRONG,H2XANDH3XMIGHTBECORRECT,SOTHATAMAJORITYVOTECORRECTLYCLASSIFIESXMOREPRECISELY,IFTHEERRORRATESOFLHYPOTHESESHIAREALLEQUALTOPL/2ANDIFTHEERRORSAREINDEPENDENT,THENTHEPROBABILITYTHATBINOMIALDISTRIBUTIONWHEREMORETHANL/2HYPOTHESESAREWRONGFIGURE1SHOWSTHISAREAFORASIMULATEDENSEMBLEOF21HYPOTHESES,EACHHAVINGANERRORRATEOF03THEAREAUNDERTHECURVEFOR11ORMOREHYPOTHESESBEINGSIMULTANEOUSLYWRONGIS0026,WHICHISMUCHLESSTHANTHEERRORRATEOFTHEINDIVIDUALHYPOTHESESOFCOURSE,IFTHEINDIVIDUALHYPOTHESESMAKEUNCORRELATEDERRORSATRATESEXCEEDING05,THENTHEERRORRATEOFTHEVOTEDENSEMBLEINCREASESASARESULTOFTHEVOTINGHENCE,THEKEYTOSUCCESSFULENSEMBLEMETHODSISTOCONSTRUCTINDIVIDUALCLASSIFIERSWITHERRORRATESBELOW05WHOSEERRORSAREATLEASTSOMEWHATUNCORRELATEDMETHODSFORCONSTRUCTINGENSEMBLESMANYMETHODSFORCONSTRUCTINGENSEMBLESHAVEBEENDEVELOPEDSOMEMETHODSAREGENERAL,ANDTHEYCANBEAPPLIEDTOANYLEARNINGALGORITHMOTHERMETHODSARESPECIFICTOPARTICULARALGORITHMSIBEGINBYREVIEWINGTHEGENERALTECHNIQUESSUBSAMPLINGTHETRAININGEXAMPLESTHEFIRSTMETHODMANIPULATESTHETRAININGEXAMPLESTOGENERATEMULTIPLE3HYPOTHESESTHELEARNINGALGORITHMISRUNSEVERALTIMES,EACHTIMEWITHADIFFERENTSUBSETOFTHETRAININGEXAMPLESTHISTECHNIQUEWORKSESPECIALLYWELLFORUNSTABLELEARNINGALGORITHMSALGORITHMSWHOSEOUTPUTCLASSIFIERUNDERGOESMAJORCHANGESINRESPONSETOSMALLCHANGESINTHETRAININGDATADECISIONTREE,NEURALNETWORK,ANDRULELEARNINGALGORITHMSAREALLUNSTABLELINEARREGRESSION,NEARESTNEIGHBOR,ANDLINEARTHRESHOLDALGORITHMSAREGENERALLYSTABLETHEMOSTSTRAIGHTFORWARDWAYOFMANIPULATINGTHETRAININGSETISCALLEDBAGGINGONEACHRUN,BAGGINGPRESENTSTHELEARNINGALGORITHMWITHATRAININGSETTHATCONSISTOFASAMPLEOFMTRAININGEXAMPLESDRAWNRANDOMLYWITHREPLACEMENTFROMTHEORIGINALTRAININGSETOFMITEMSSUCHATRAININGSETISCALLEDABOOTSTRAPREPLICATEOFTHEORIGINALTRAININGSET,ANDTHETECHNIQUEISCALLEDBOOTSTRAPAGG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