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1、Machine-LearningResearchFourCurrentDirectionsThomasG.Dietterich■Machine-learningresearchhasbeenmakinggreatprogressinmanydirections.Thisarticlesummarizesfourofthesedirectionsanddiscussessomecurrentopenproblems.Thefourdirectionsare(1)theimprovementofclassificationaccuracybylearningensemblesofcl
2、assifiers,(2)methodsforscalingupsupervisedlearningalgorithms,(3)reinforcementlearning,and(4)thelearningofcomplexstochasticmodels.Thelastfiveyearshaveseenanexplosioninmachine-learningresearch.Thisexplosionhasmanycauses:First,separateresearchcommunitiesinsymbolicmachinelearning,computationlearn
3、ingtheory,neuralnetworks,statistics,andpatternrecognitionhavediscoveredoneanotherandbeguntoworktogether.Second,machine-learningtechniquesarebeingappliedtonewkindsofproblem,includingknowledgediscoveryindatabases,languageprocessing,robotcontrol,andcombinatorialoptimization,aswellastomoretraditi
4、onalproblemssuchasspeechrecognition,facerecognition,handwritingrecognition,medicaldataanalysis,andgameplaying.Inthisarticle,Iselectedfourtopicswithinmachinelearningwheretherehasbeenalotofrecentactivity.ThepurposeofthearticleistodescribetheresultsintheseareastoabroaderAIaudienceandtosketchsome
5、oftheopenresearchproblems.Thetopicareasare(1)ensemblesofclassifiers,(2)methodsforscalingupsupervisedlearningalgorithms,(3)reinforcementlearning,and(4)thelearningofcomplexstochasticmodels.Thereadershouldbecautionedthatthisarticleisnotacomprehensivereviewofeachofthesetopics.Rather,mygoalistopro
6、videarepresentativesampleoftheresearchineachofthesefourareas.Ineachoftheareas,therearemanyotherpapersthatdescriberelevantwork.IapologizetothoseauthorswhoseworkIwasunabletoincludeinthearticle.EnsemblesofClassifiersThefirsttopicconcernsmethodsforimprovingaccuracyinsupervisedlearning.Ibeginbyint
7、roducingsomenotation.Insupervisedlearning,alearningprogramisgiventrainingexamplesoftheform{(x1,y1),…,(xm,ym)}forsomeunknownfunctiony=f(x).Thexivaluesaretypicallyvectorsoftheformwhosecomponentsarediscreteorrealvalued,suchash
