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1、INTRODUCTIONTOMACHINELEARNINGIntroductiontoMachineLearningAlexSmolaandS.V.N.VishwanathanYahoo!LabsSantaClara{and{DepartmentsofStatisticsandComputerSciencePurdueUniversity{and{CollegeofEngineeringandComputerScienceAustralianNationalUniversitypublishedbythepresssyn
2、dicateoftheuniversityofcambridgeThePittBuilding,TrumpingtonStreet,Cambridge,UnitedKingdomcambridgeuniversitypressTheEdinburghBuilding,CambridgeCB22RU,UK40West20thStreet,NewYork,NY10011{4211,USA477WilliamstownRoad,PortMelbourne,VIC3207,AustraliaRuizdeAlarcon13,28
3、014Madrid,SpainDockHouse,TheWaterfront,CapeTown8001,SouthAfricahttp://www.cambridge.orgcCambridgeUniversityPress2008Thisbookisincopyright.Subjecttostatutoryexceptionandtotheprovisionsofrelevantcollectivelicensingagreements,noreproductionofanypartmaytakeplacewitho
4、utthewrittenpermissionofCambridgeUniversityPress.Firstpublished2008PrintedintheUnitedKingdomattheUniversityPress,CambridgeTypefaceMonotypeTimes10/13ptSystemLATEX2"[AlexanderJ.SmolaandS.V.N.Vishwanathan]AcataloguerecordforthisbookisavailablefromtheBritishLibraryLi
5、braryofCongressCataloguinginPublicationdataavailableISBN0521825830hardbackAuthor:vishyRevision:252Timestamp:October1,2010URL:svn://smola@repos.stat.purdue.edu/thebook/trunk/Book/thebook.texContentsPrefacepage11Introduction31.1ATasteofMachineLearning31.1.1Applicat
6、ions31.1.2Data71.1.3Problems91.2ProbabilityTheory121.2.1RandomVariables121.2.2Distributions131.2.3MeanandVariance151.2.4Marginalization,Independence,Conditioning,andBayesRule161.3BasicAlgorithms201.3.1NaiveBayes221.3.2NearestNeighborEstimators241.3.3ASimpleClassi
7、 er271.3.4Perceptron291.3.5K-Means322DensityEstimation372.1LimitTheorems372.1.1FundamentalLaws382.1.2TheCharacteristicFunction422.1.3TailBounds452.1.4AnExample482.2ParzenWindows512.2.1DiscreteDensityEstimation512.2.2SmoothingKernel522.2.3ParameterEstimation542.2.
8、4Silverman'sRule572.2.5Watson-NadarayaEstimator592.3ExponentialFamilies602.3.1Basics60vvi0Contents2.3.2Examples622.4Estimation662.4.1MaximumLikelihoodEstimatio
