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1、1MachineLearningResearchFourCurrentDirectionsThomasG.Dietterich■Machinelearningresearchhasbeenmakinggreatprogressinmanydirections.Thisarticlesummarizesfourofthesedirectionsdiscussessomecurrentopenproblems.Thefourdirectio

2、nsare(1)theimprovementofclassificationaccuracybylearningensemblesofclassifiers(2)methodsfscalingupsupervisedlearningalgithms(3)reinfcementlearning(4)thelearningofcomplexstochasticmodels.Thelastfiveyearshaveseenanexplosio

3、ninmachinelearningresearch.Thisexplosionhasmanycauses:Firstseparateresearchcommunitiesinsymbolicmachinelearningcomputationlearningtheyneuralwksstatisticspatternrecognitionhavediscoveredoneanotherbeguntowktogether.Secondm

4、achinelearningtechniquesarebeingappliedtonewkindsofproblemincludingknowledgediscoveryindatabaseslanguageprocessingrobotcontrolcombinatialoptimizationaswellastometraditionalproblemssuchasspeechrecognitionfacerecognitionhw

5、ritingrecognitionmedicaldataanalysisgameplaying.InthisarticleIedfourtopicswithinmachinelearningwheretherehasbeenalotofrecentactivity.ThepurposeofthearticleistodescribetheresultsintheseareastoabroaderAIaudiencetosketchsom

6、eoftheopenresearchproblems.Thetopicareasare(1)ensemblesofclassifiers(2)methodsfscalingupsupervisedlearningalgithms(3)reinfcementlearning(4)thelearningofcomplexstochasticmodels.Thereadershouldbecautionedthatthisarticleisn

7、otacomprehensivereviewofeachofthesetopics.Rathermygoalistoprovidearepresentativesampleoftheresearchineachofthesefourareas.Ineachoftheareastherearemanyotherpapersthatdescriberelevantwk.IapologizetothoseauthswhosewkIwasuna

8、bletoincludeinthearticle.EnsemblesEnsemblesofofClassifiersClassifiersThefirsttopicconcernsmethodsfimprovingaccuracyinsupervisedlearning.Ibeginbyintroducingsomenotation.Insupervisedlearningalearningprogramisgiventraininge

9、xamplesofthefm(x1y1)…(xmym)fsomeunknownfunctiony=f(x).Thexivaluesaretypicallyvectsofthefmwhosecomponentsarediscreterealvaluedsuchasheightweightcolage.ThesearealsocalledthefeatureofXiIusethenotationXijto.refertothejth3hyp

10、otheses.Thelearningalgithmisrunseveraltimeseachtimewithadifferentsubsetofthetrainingexamples.Thistechniquewksespeciallywellfunstablelearningalgithmsalgithmswhoseoutputclassifierundergoesmajchangesinresponsetosmallchanges

11、inthetrainingdata.Decisiontreeneuralwkrulelearningalgithmsareallunstable.Linearregressionnearestneighblinearthresholdalgithmsaregenerallystable.Themoststraightfwardwayofmanipulatingthetrainingsetiscalledbagging.Oneachrun

12、baggingpresentsthelearningalgithmwithatrainingsetthatconsistofasampleofmtrainingexamplesdrawnromlywithreplacementfromtheiginaltrainingsetofmitems.Suchatrainingsetiscalledabootstrapreplicateoftheiginaltrainingsetthetechni

13、queiscalledbootstrapaggregation(Breiman1996a).Eachbootstrapreplicatecontainsontheaverage63.2percentoftheiginalsetwithseveraltrainingexamplesappearingmultipletimes.Anothertrainingsetsamplingmethodistoconstructthetrainings

14、etsbyleavingoutdisjointsubsets.Then10overlappingtrainingsetscanbedividedromlyinto10disjointsubsets.Then10overlappingtrainingsetscanbeconstructedbypingoutadifferentisusedtoconstructtrainingsetsftenfoldcrossvalidationsoens

15、emblesconstructedinthiswayaresometimescalledcrossvalidatedcommittees(ParmantoMunroDoyle1996).ThethirdmethodfmanipulatingthetrainingsetisillustratedbytheADABOOSTalgithmdevelopedbyFreundSchapire(19961995)showninfigure2.Lik

16、ebaggingADABOOSTmanipulatesthetrainingexamplestogeneratemultiplehypotheses.ADABOOSTmaintainsaprobabilitydistributionpi(x)overthetrainingexamples.Ineachiterationiitdrawsatrainingsetofsizembysamplingwithreplacementaccdingt

17、otheprobabilitydistributionpi(x).Thelearningalgithmisthenappliedtoproduceaclassifierhi.Theerrrate￡iofthisclassifieronthetrainingexamples(weightedaccdingtopi(x))iscomputedusedtoadjusttheprobabilitydistributiononthetrainin

18、gexamples.(Infigure2notethattheprobabilitydistributionisobtainedbynmalizingasetofweightswi(i)overthetrainingexamples.)Theeffectofthechangeinweightsistoplacemeweightonexamplesthatweremisclassifiedbyhilessweightonexamplest

19、hatwerecrectlyclassified.InsubsequentiterationstherefeADABOOSTconstructsprogressivelymedifficultlearningproblems.Thefinalclassifierhiisconstructsbyaweightedvoteoftheindividualclassifiers.Eachclassifierisweightedaccdingto

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