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1、2178IEEETRANSACTIONSONINFORMATIONTHEORY,VOL.44,NO.6,OCTOBER1998LearningPatternClassificationÐASurveySanjeevR.Kulkarni,SeniorMember,IEEE,GaborLugosi,andSantoshS.Venkatesh,´Member,IEEE(InvitedPaper)AbstractÐClassicalandrecentresultsinstatisticalpatternternclassification,stochasticcontrol,re
2、inforcementlearning,recognitionandlearningtheoryarereviewedinatwo-classclustering,etc.patternclassificationsetting.ThisbasicmodelbestillustratesWorkontheseareasspansanumberoffieldsandmanyintuitionandanalysistechniqueswhilestillcontainingtheessen-years.Inthelastseveraldecades,therewasworki
3、nthetialfeaturesandservingasaprototypeformanyapplications.Topicsdiscussedincludenearestneighbor,kernel,andhistogram1940'sand1950'sinareassuchasstatistics,informationmethods,Vapnik±Chervonenkistheory,andneuralnetworks.Thetheory,cybernetics,andearlyworkonneuralnetworksthatpresentationandt
4、helarge(thoghnonexhaustive)listofreferencesledtotremendousprogressand,infact,establishedseveralisgearedtoprovideausefuloverviewofthisfieldforbothnewfieldsofactivity.Continuedworkintheseareas,slightlyspecialistsandnonspecialists.laterworkonsystemsandcontroltheory,patternrecognition,IndexTe
5、rmsÐClassification,learning,statisticalpatternrecog-andoptimization,andmorerecentlytheexplosionofworknition,surveyreview.onneuralnetworksandothertopicssuchascomputationallearningtheory,areallpartofthisgeneralarea.I.INTRODUCTIONInthispaper,wefocusonaveryspecificsubsetofthisworkdealingwitht
6、wo-classpatternclassification.Thisproblem,HEgoaloflearningtheoryistoprovideanswerstobasicdefinedbelow,servesasaprototypeformanyreal-lifelearningTquestionssuchas:problems,whilethemathematicalsimplicityofthemodel·Whatproblemscanandcannotbelearned?allowsustogaininsightintomostofthedifficultie
7、sarising·Howmuchdataisrequired?inlearningproblems.However,thismodelbynomeans·Whataregoodalgorithmsforlearningfromexamples?coversallaspectsoflearning.Forexample,theassumptionHence,learningtheoryattemptstodelineatethefundamentalofhavingonlytwoclasseshidesmanyofthebasicdifficulties