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[美] 哈斯蒂 (Hastie T) 著 / 世界图书出版公司 / 2009-01 / 平装
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统计学习基础:(英文版)
Thelearningproblemsthatweconsidercanberoughlycategorizedaseithersupervisedorunsupervised.Insupervisedlearning,thegoalistopredictthevalueofanoutcomemeasurebasedonanumberofinputmeasures;inunsupervisedlearning,thereisnooutcomemeasure,andthegoalistodescribetheassociationsandpatternsamongasetofinputmeasures.
作者:(德国)T.黑斯蒂(Trevor Hastie)
Preface1IntroductionOverviewofSupervisedLearning2.1Introduction2.2VariableTypesandTerminology2.3TwoSimpleApproachestoPrediction:LeastSquaresandNearestNeighbors2.3.1LinearModelsandLeastSquares2.3.2Nearest-NeighborMethods2.3.3FromLeastSquarestoNearestNeighbors2.4StatisticalDecisionTheory2.5LocalMethodsinHighDimensions2.6StatisticalModels,SupervisedLearningandFunctionApproximation2.6.1AStatisticalModelfortheJointDistributionPr(X,Y)2.6.2SupervisedLearning2.6.3FunctionApproximation2.7StructuredRegressionModels2.7.1DifficultyoftheProblem2.8ClassesofRestrictedEstimators2.8.1RoughnessPenaltyandBayesianMethods2.8.2KernelMethodsandLocalRegression2.8.3BasisFunctionsandDictionaryMethods2.9ModelSelectionandtheBias-VarianceTradeoffBibliographicNotesExercises3LinearMethodsforRegression3.1Introduction3.2LinearRegressionModelsandLeastSquares3.2.1Example:ProstateCancer3.2.2TheGanss-MarkovTheorem3.3MultipleRegressionfromSimpleUnivariateRegression3.3.1MultipleOutputs3.4SubsetSelectionandCoefficientShrinkage3.4.1SubsetSelection3.4.2ProstateCancerDataExamplefContinued)3.4.3ShrinkageMethods3.4.4MethodsUsingDerivedInputDirections3.4.5Discussion:AComparisonoftheSelectionandShrinkageMethods3.4.6MultipleOutcomeShrinkageandSelection3.5CompntationalConsiderationsBibliographicNotesExercises4LinearMethodsforClassification4.1Introduction4.2LinearRegressionofanIndicatorMatrix4.3LinearDiscriminantAnalysis4.3.1RegularizedDiscriminantAnalysis4.3.2ComputationsforLDA4.3.3Reduced-RankLinearDiscriminantAnalysis4.4LogisticRegression4.4.1FittingLogisticRegressionModels4.4.2Example:SouthAfricanHeartDisease4.4.3QuadraticApproximationsandInference4.4.4LogisticRegressionorLDA74.5SeparatingHyperplanes4.5.1RosenblattsPerceptronLearningAlgorithm4.5.2OptimalSeparatingHyperplanesBibliographicNotesExercises5BasisExpansionsandRegularizatlon5.1Introduction5.2PiecewisePolynomialsandSplines5.2.1NaturalCubicSplines5.2.2Example:SouthAfricanHeartDisease(Continued)5.2.3Example:PhonemeRecognition5.3FilteringandFeatureExtraction5.4SmoothingSplines5.4.1DegreesofFreedomandSmootherMatrices5.5AutomaticSelectionoftheSmoothingParameters5.5.1FixingtheDegreesofFreedom5.5.2TheBias-VarianceTradeoff5.6NonparametricLogisticRegression5.7MultidimensionalSplines5.8RegularizationandReproducingKernelHilbertSpaces..5.8.1SpacesofPhnctionsGeneratedbyKernels5.8.2ExamplesofRKHS5.9WaveletSmoothing5.9.1WaveletBasesandtheWaveletTransform5.9.2AdaptiveWaveletFilteringBibliographicNotesExercisesAppendix:ComputationalConsiderationsforSplinesAppendix:B-splinesAppendix:ComputationsforSmoothingSplines6KernelMethods6.1One-DimensionalKernelSmoothers6.1.1LocalLinearRegression6.1.2LocalPolynomialRegression6.2SelectingtheWidthoftheKernel6.3LocalRegressioninJap6.4StructuredLocalRegressionModelsin]ap6.4.1StructuredKernels6.4.2StructuredRegressionFunctions6.5LocalLikelihoodandOtherModels6.6KernelDensityEstimationandClassification6.6.1KernelDensityEstimation6.6.2KernelDensityClassification6.6.3TheNaiveBayesClassifier6.7RadialBasisFunctionsandKernels6.8MixtureModelsforDensityEstimationandClassification6.9ComputationalConsiderationsBibliographicNotesExercises7ModelAssessmentandSelection7.1Introduction7.2Bias,VarianceandModelComplexity7.3TheBias-VarianceDecomposition7.3.1Example:Bias-VarianceTradeoff7.4OptimismoftheTrainingErrorRate7.5EstimatesofIn-SamplePredictionError7.6TheEffectiveNumberofParameters7.7TheBayesianApproachandBIC7.8MinimumDescriptionLength7.9VapnikChernovenkisDimension7.9.1Example(Continued)7.10Cross-Validation7.11BootstrapMethods7.11.1Example(Continued)BibliographicNotesExercises8ModelInferenceandAveraging8.1Introduction8.2TheBootstrapandMaximumLikelihoodMethods8.2.1ASmoothingExample8.2.2MaximumLikelihoodInference8.2.3BootstrapversusMaximumLikelihood8.3BayesianMethods8.4RelationshipBetweentheBootstrapandBayesianInference8.5TheEMAlgorithm8.5.1Two-ComponentMixtureModel8.5.2TheEMAlgorithminGeneral8.5.3EMasaMaximization-MaximizationProcedure8.6MCMCforSamplingfromthePosterior8.7Bagging8.7.1Example:TreeswithSimulatedData8.8ModelAveragingandStacking8.9StochasticSearch:BumpingBibliographicNotesExercises9AdditiveModels,Trees,andRelatedMethods9.1GeneralizedAdditiveModels9.1.1FittingAdditiveModels9.1.2Example:AdditiveLogisticRegression9.1.3Summary9.2TreeBasedMethods10BoostingandAdditiveTrees11NeuralNetworks12SupportVectorMachinesandFlexibleDiscriminants13PrototypeMethodsandNearest-Neighbors14UnsupervisedLearningReferencesAuthorIndexIndex
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