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1、Machine-LearningResearchFourCurrentDirectionsThomasG.Dietterich■Machine-learningresearchhasbeenmakinggreatprogressinmanydirections.Thisarticlesummarizesfourofthesedirectionsanddiscussessomecurrentopenproblems.Thefourdirectionsare(1)theimprovementofclassificationaccurac
2、ybylearningensemblesofclassifiers,(2)methodsforscalingupsupervisedlearningalgorithms,(3)reinforcementlearning,and(4)thelearningofcomplexstochasticmodels.Thelastfiveyearshaveseenanexplosioninmachine-learningresearch.Thisexplosionhasmanycauses:First,separateresearchcommu
3、nitiesinsymbolicmachinelearning,computationlearningtheory,neuralnetworks,statistics,andpatternrecognitionhavediscoveredoneanotherandbeguntoworktogether.Second,machine-learningtechniquesarebeingappliedtonewkindsofproblem,includingknowledgediscoveryindatabases,languagepr
4、ocessing,robotcontrol,andcombinatorialoptimization,aswellastomoretraditionalproblemssuchasspeechrecognition,facerecognition,handwritingrecognition,medicaldataanalysis,andgameplaying.Inthisarticle,Iselectedfourtopicswithinmachinelearningwheretherehasbeenalotofrecentacti
5、vity.ThepurposeofthearticleistodescribetheresultsintheseareastoabroaderAIaudienceandtosketchsomeoftheopenresearchproblems.Thetopicareasare(1)ensemblesofclassifiers,(2)methodsforscalingupsupervisedlearningalgorithms,(3)reinforcementlearning,and(4)thelearningofcomplexsto
6、chasticmodels.Thereadershouldbecautionedthatthisarticleisnotacomprehensivereviewofeachofthesetopics.Rather,mygoalistoprovidearepresentativesampleoftheresearchineachofthesefourareas.Ineachoftheareas,therearemanyotherpapersthatdescriberelevantwork.Iapologizetothoseauthor
7、swhoseworkIwasunabletoincludeinthearticle.EnsemblesofClassifiersThefirsttopicconcernsmethodsforimprovingaccuracyinsupervisedlearning.Ibeginbyintroducingsomenotation.Insupervisedlearning,alearningprogramisgiventrainingexamplesoftheform{(x1,y1),…,(xm,ym)}forsomeunknownfu
8、nctiony=f(x).Thexivaluesaretypicallyvectorsoftheformwhosecomponentsarediscreteorrealvalued,suchash
