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1、MachineLearninginComputerVisionbyN.SEBEUniversityofAmsterdam,TheNetherlandsIRACOHENHPResearchLabs,U.S.A.ASHUTOSHGARGGoogleInc.,U.S.A.andTHOMASS.HUANGUniversityofIllinoisatUrbana-Champaign,Urbana,IL,U.S.A.AC.I.P.CataloguerecordforthisbookisavailablefromtheLibrary
2、ofCongress.ISBN-101-4020-3274-9(HB)SpringerDordrecht,Berlin,Heidelberg,NewYorkISBN-101-4020-3275-7(e-book)SpringerDordrecht,Berlin,Heidelberg,NewYorkISBN-13978-1-4020-3274-5(HB)SpringerDordrecht,Berlin,Heidelberg,NewYorkISBN-13978-1-4020-3275-2(e-book)SpringerDo
3、rdrecht,Berlin,Heidelberg,NewYorkPublishedbySpringer,P.O.Box17,3300AADordrecht,TheNetherlands.Printedonacid-freepaperAllRightsReserved©2005SpringerNopartofthisworkmaybereproduced,storedinaretrievalsystem,ortransmittedinanyformorbyanymeans,electronic,mechanical,p
4、hotocopying,microfilming,recordingorotherwise,withoutwrittenpermissionfromthePublisher,withtheexceptionofanymaterialsuppliedspecificallyforthepurposeofbeingenteredandexecutedonacomputersystem,forexclusiveusebythepurchaserofthework.PrintedintheNetherlands.Tomypar
5、entsNicuToMeravandYonatanIraTomyparentsAsutoshTomystudents:Past,present,andfutureTomContentsForewordxiPrefacexiii1.INTRODUCTION11ResearchIssuesonLearninginComputerVision22OverviewoftheBook63Contributions122.THEORY:PROBABILISTICCLASSIFIERS151Introduction152Prelim
6、inariesandNotations182.1MaximumLikelihoodClassification182.2InformationTheory192.3Inequalities203BayesOptimalErrorandEntropy204AnalysisofClassificationErrorofEstimated(Mismatched)Distribution274.1HypothesisTestingFramework284.2ClassificationFramework305DensityofDis
7、tributions315.1DistributionalDensity335.2RelatingtoClassificationError376ComplexProbabilisticModelsandSmallSampleEffects407Summary41viMACHINELEARNINGINCOMPUTERVISION3.THEORY:GENERALIZATIONBOUNDS451Introduction452Preliminaries473AMarginDistributionBasedBound493.1P
8、rovingtheMarginDistributionBound494Analysis574.1ComparisonwithExistingBounds595Summary644.THEORY:SEMI-SUPERVISEDLEARNING651Introduction652PropertiesofClassification673
