统计学习基础:(英文版)

统计学习基础
分享
扫描下方二维码分享到微信
打开微信,点击右上角”+“,
使用”扫一扫“即可将网页分享到朋友圈。
作者: [美] (Hastie T)
出版社: 世界图书出版公司
2009-01
版次: 1
ISBN: 9787506292313
定价: 88.00
装帧: 平装
开本: 32开
纸张: 胶版纸
页数: 533页
正文语种: 英语
  •   Thelearningproblemsthatweconsidercanberoughlycategorizedaseithersupervisedorunsupervised.Insupervisedlearning,thegoalistopredictthevalueofanoutcomemeasurebasedonanumberofinputmeasures;inunsupervisedlearning,thereisnooutcomemeasure,andthegoalistodescribetheassociationsandpatternsamongasetofinputmeasures. 作者:(德国)T.黑斯蒂(Trevor Hastie) Preface
    1IntroductionOverviewofSupervisedLearning
    2.1Introduction
    2.2VariableTypesandTerminology
    2.3TwoSimpleApproachestoPrediction:LeastSquaresandNearestNeighbors
    2.3.1LinearModelsandLeastSquares
    2.3.2Nearest-NeighborMethods
    2.3.3FromLeastSquarestoNearestNeighbors
    2.4StatisticalDecisionTheory
    2.5LocalMethodsinHighDimensions
    2.6StatisticalModels,SupervisedLearningandFunctionApproximation
    2.6.1AStatisticalModelfortheJointDistributionPr(X,Y)
    2.6.2SupervisedLearning
    2.6.3FunctionApproximation
    2.7StructuredRegressionModels
    2.7.1DifficultyoftheProblem
    2.8ClassesofRestrictedEstimators
    2.8.1RoughnessPenaltyandBayesianMethods
    2.8.2KernelMethodsandLocalRegression
    2.8.3BasisFunctionsandDictionaryMethods
    2.9ModelSelectionandtheBias-VarianceTradeoff
    BibliographicNotes
    Exercises

    3LinearMethodsforRegression
    3.1Introduction
    3.2LinearRegressionModelsandLeastSquares
    3.2.1Example:ProstateCancer
    3.2.2TheGanss-MarkovTheorem
    3.3MultipleRegressionfromSimpleUnivariateRegression
    3.3.1MultipleOutputs
    3.4SubsetSelectionandCoefficientShrinkage
    3.4.1SubsetSelection
    3.4.2ProstateCancerDataExamplefContinued)
    3.4.3ShrinkageMethods
    3.4.4MethodsUsingDerivedInputDirections
    3.4.5Discussion:AComparisonoftheSelectionandShrinkageMethods
    3.4.6MultipleOutcomeShrinkageandSelection
    3.5CompntationalConsiderations
    BibliographicNotes
    Exercises

    4LinearMethodsforClassification
    4.1Introduction
    4.2LinearRegressionofanIndicatorMatrix
    4.3LinearDiscriminantAnalysis
    4.3.1RegularizedDiscriminantAnalysis
    4.3.2ComputationsforLDA
    4.3.3Reduced-RankLinearDiscriminantAnalysis
    4.4LogisticRegression
    4.4.1FittingLogisticRegressionModels
    4.4.2Example:SouthAfricanHeartDisease
    4.4.3QuadraticApproximationsandInference
    4.4.4LogisticRegressionorLDA7
    4.5SeparatingHyperplanes
    4.5.1RosenblattsPerceptronLearningAlgorithm
    4.5.2OptimalSeparatingHyperplanes
    BibliographicNotes
    Exercises

    5BasisExpansionsandRegularizatlon
    5.1Introduction
    5.2PiecewisePolynomialsandSplines
    5.2.1NaturalCubicSplines
    5.2.2Example:SouthAfricanHeartDisease(Continued)
    5.2.3Example:PhonemeRecognition
    5.3FilteringandFeatureExtraction
    5.4SmoothingSplines
    5.4.1DegreesofFreedomandSmootherMatrices
    5.5AutomaticSelectionoftheSmoothingParameters
    5.5.1FixingtheDegreesofFreedom
    5.5.2TheBias-VarianceTradeoff
    5.6NonparametricLogisticRegression
    5.7MultidimensionalSplines
    5.8RegularizationandReproducingKernelHilbertSpaces..
    5.8.1SpacesofPhnctionsGeneratedbyKernels
    5.8.2ExamplesofRKHS
    5.9WaveletSmoothing
    5.9.1WaveletBasesandtheWaveletTransform
    5.9.2AdaptiveWaveletFiltering
    BibliographicNotes
    Exercises
    Appendix:ComputationalConsiderationsforSplines
    Appendix:B-splines
    Appendix:ComputationsforSmoothingSplines

    6KernelMethods
    6.1One-DimensionalKernelSmoothers
    6.1.1LocalLinearRegression
    6.1.2LocalPolynomialRegression
    6.2SelectingtheWidthoftheKernel
    6.3LocalRegressioninJap
    6.4StructuredLocalRegressionModelsin]ap
    6.4.1StructuredKernels
    6.4.2StructuredRegressionFunctions
    6.5LocalLikelihoodandOtherModels
    6.6KernelDensityEstimationandClassification
    6.6.1KernelDensityEstimation
    6.6.2KernelDensityClassification
    6.6.3TheNaiveBayesClassifier
    6.7RadialBasisFunctionsandKernels
    6.8MixtureModelsforDensityEstimationandClassification
    6.9ComputationalConsiderations
    BibliographicNotes
    Exercises

    7ModelAssessmentandSelection
    7.1Introduction
    7.2Bias,VarianceandModelComplexity
    7.3TheBias-VarianceDecomposition
    7.3.1Example:Bias-VarianceTradeoff
    7.4OptimismoftheTrainingErrorRate
    7.5EstimatesofIn-SamplePredictionError
    7.6TheEffectiveNumberofParameters
    7.7TheBayesianApproachandBIC
    7.8MinimumDescriptionLength
    7.9VapnikChernovenkisDimension
    7.9.1Example(Continued)
    7.10Cross-Validation
    7.11BootstrapMethods
    7.11.1Example(Continued)
    BibliographicNotes
    Exercises

    8ModelInferenceandAveraging
    8.1Introduction
    8.2TheBootstrapandMaximumLikelihoodMethods
    8.2.1ASmoothingExample
    8.2.2MaximumLikelihoodInference
    8.2.3BootstrapversusMaximumLikelihood
    8.3BayesianMethods
    8.4RelationshipBetweentheBootstrapandBayesianInference
    8.5TheEMAlgorithm
    8.5.1Two-ComponentMixtureModel
    8.5.2TheEMAlgorithminGeneral
    8.5.3EMasaMaximization-MaximizationProcedure
    8.6MCMCforSamplingfromthePosterior
    8.7Bagging
    8.7.1Example:TreeswithSimulatedData
    8.8ModelAveragingandStacking
    8.9StochasticSearch:Bumping
    BibliographicNotes
    Exercises

    9AdditiveModels,Trees,andRelatedMethods
    9.1GeneralizedAdditiveModels
    9.1.1FittingAdditiveModels
    9.1.2Example:AdditiveLogisticRegression
    9.1.3Summary
    9.2TreeBasedMethods
    10BoostingandAdditiveTrees
    11NeuralNetworks
    12SupportVectorMachinesandFlexibleDiscriminants
    13PrototypeMethodsandNearest-Neighbors
    14UnsupervisedLearning
    References
    AuthorIndex
    Index
  • 内容简介:
      Thelearningproblemsthatweconsidercanberoughlycategorizedaseithersupervisedorunsupervised.Insupervisedlearning,thegoalistopredictthevalueofanoutcomemeasurebasedonanumberofinputmeasures;inunsupervisedlearning,thereisnooutcomemeasure,andthegoalistodescribetheassociationsandpatternsamongasetofinputmeasures.
  • 作者简介:
    作者:(德国)T.黑斯蒂(Trevor Hastie)
  • 目录:
    Preface
    1IntroductionOverviewofSupervisedLearning
    2.1Introduction
    2.2VariableTypesandTerminology
    2.3TwoSimpleApproachestoPrediction:LeastSquaresandNearestNeighbors
    2.3.1LinearModelsandLeastSquares
    2.3.2Nearest-NeighborMethods
    2.3.3FromLeastSquarestoNearestNeighbors
    2.4StatisticalDecisionTheory
    2.5LocalMethodsinHighDimensions
    2.6StatisticalModels,SupervisedLearningandFunctionApproximation
    2.6.1AStatisticalModelfortheJointDistributionPr(X,Y)
    2.6.2SupervisedLearning
    2.6.3FunctionApproximation
    2.7StructuredRegressionModels
    2.7.1DifficultyoftheProblem
    2.8ClassesofRestrictedEstimators
    2.8.1RoughnessPenaltyandBayesianMethods
    2.8.2KernelMethodsandLocalRegression
    2.8.3BasisFunctionsandDictionaryMethods
    2.9ModelSelectionandtheBias-VarianceTradeoff
    BibliographicNotes
    Exercises

    3LinearMethodsforRegression
    3.1Introduction
    3.2LinearRegressionModelsandLeastSquares
    3.2.1Example:ProstateCancer
    3.2.2TheGanss-MarkovTheorem
    3.3MultipleRegressionfromSimpleUnivariateRegression
    3.3.1MultipleOutputs
    3.4SubsetSelectionandCoefficientShrinkage
    3.4.1SubsetSelection
    3.4.2ProstateCancerDataExamplefContinued)
    3.4.3ShrinkageMethods
    3.4.4MethodsUsingDerivedInputDirections
    3.4.5Discussion:AComparisonoftheSelectionandShrinkageMethods
    3.4.6MultipleOutcomeShrinkageandSelection
    3.5CompntationalConsiderations
    BibliographicNotes
    Exercises

    4LinearMethodsforClassification
    4.1Introduction
    4.2LinearRegressionofanIndicatorMatrix
    4.3LinearDiscriminantAnalysis
    4.3.1RegularizedDiscriminantAnalysis
    4.3.2ComputationsforLDA
    4.3.3Reduced-RankLinearDiscriminantAnalysis
    4.4LogisticRegression
    4.4.1FittingLogisticRegressionModels
    4.4.2Example:SouthAfricanHeartDisease
    4.4.3QuadraticApproximationsandInference
    4.4.4LogisticRegressionorLDA7
    4.5SeparatingHyperplanes
    4.5.1RosenblattsPerceptronLearningAlgorithm
    4.5.2OptimalSeparatingHyperplanes
    BibliographicNotes
    Exercises

    5BasisExpansionsandRegularizatlon
    5.1Introduction
    5.2PiecewisePolynomialsandSplines
    5.2.1NaturalCubicSplines
    5.2.2Example:SouthAfricanHeartDisease(Continued)
    5.2.3Example:PhonemeRecognition
    5.3FilteringandFeatureExtraction
    5.4SmoothingSplines
    5.4.1DegreesofFreedomandSmootherMatrices
    5.5AutomaticSelectionoftheSmoothingParameters
    5.5.1FixingtheDegreesofFreedom
    5.5.2TheBias-VarianceTradeoff
    5.6NonparametricLogisticRegression
    5.7MultidimensionalSplines
    5.8RegularizationandReproducingKernelHilbertSpaces..
    5.8.1SpacesofPhnctionsGeneratedbyKernels
    5.8.2ExamplesofRKHS
    5.9WaveletSmoothing
    5.9.1WaveletBasesandtheWaveletTransform
    5.9.2AdaptiveWaveletFiltering
    BibliographicNotes
    Exercises
    Appendix:ComputationalConsiderationsforSplines
    Appendix:B-splines
    Appendix:ComputationsforSmoothingSplines

    6KernelMethods
    6.1One-DimensionalKernelSmoothers
    6.1.1LocalLinearRegression
    6.1.2LocalPolynomialRegression
    6.2SelectingtheWidthoftheKernel
    6.3LocalRegressioninJap
    6.4StructuredLocalRegressionModelsin]ap
    6.4.1StructuredKernels
    6.4.2StructuredRegressionFunctions
    6.5LocalLikelihoodandOtherModels
    6.6KernelDensityEstimationandClassification
    6.6.1KernelDensityEstimation
    6.6.2KernelDensityClassification
    6.6.3TheNaiveBayesClassifier
    6.7RadialBasisFunctionsandKernels
    6.8MixtureModelsforDensityEstimationandClassification
    6.9ComputationalConsiderations
    BibliographicNotes
    Exercises

    7ModelAssessmentandSelection
    7.1Introduction
    7.2Bias,VarianceandModelComplexity
    7.3TheBias-VarianceDecomposition
    7.3.1Example:Bias-VarianceTradeoff
    7.4OptimismoftheTrainingErrorRate
    7.5EstimatesofIn-SamplePredictionError
    7.6TheEffectiveNumberofParameters
    7.7TheBayesianApproachandBIC
    7.8MinimumDescriptionLength
    7.9VapnikChernovenkisDimension
    7.9.1Example(Continued)
    7.10Cross-Validation
    7.11BootstrapMethods
    7.11.1Example(Continued)
    BibliographicNotes
    Exercises

    8ModelInferenceandAveraging
    8.1Introduction
    8.2TheBootstrapandMaximumLikelihoodMethods
    8.2.1ASmoothingExample
    8.2.2MaximumLikelihoodInference
    8.2.3BootstrapversusMaximumLikelihood
    8.3BayesianMethods
    8.4RelationshipBetweentheBootstrapandBayesianInference
    8.5TheEMAlgorithm
    8.5.1Two-ComponentMixtureModel
    8.5.2TheEMAlgorithminGeneral
    8.5.3EMasaMaximization-MaximizationProcedure
    8.6MCMCforSamplingfromthePosterior
    8.7Bagging
    8.7.1Example:TreeswithSimulatedData
    8.8ModelAveragingandStacking
    8.9StochasticSearch:Bumping
    BibliographicNotes
    Exercises

    9AdditiveModels,Trees,andRelatedMethods
    9.1GeneralizedAdditiveModels
    9.1.1FittingAdditiveModels
    9.1.2Example:AdditiveLogisticRegression
    9.1.3Summary
    9.2TreeBasedMethods
    10BoostingandAdditiveTrees
    11NeuralNetworks
    12SupportVectorMachinesandFlexibleDiscriminants
    13PrototypeMethodsandNearest-Neighbors
    14UnsupervisedLearning
    References
    AuthorIndex
    Index
查看详情
好书推荐 / 更多
统计学习基础
野猪渡河
张贵兴 著
统计学习基础
东方故事集(插图本)
[法]玛格丽特·尤瑟纳尔
统计学习基础
我和我的命(梁晓声新作)
梁晓声
统计学习基础
当你起航前往伊萨卡:卡瓦菲斯诗集
[希腊]C. P. 卡瓦菲斯 著;黄灿然 译
统计学习基础
在喧嚣和寂静之间
[波]维斯瓦娃·希姆博尔斯卡 著;林洪亮 译
统计学习基础
没有男人的女人们 没有女人的男人们(新丝路文库)
[伊朗]沙赫尔努希·帕尔西普尔 著;穆宏燕 王莹
统计学习基础
中美相遇:大国外交与晚清兴衰(1784-1911)
王元崇 著
统计学习基础
人文与社会译丛:自足的世俗社会
菲尔·朱克曼 著;杨靖 译
统计学习基础
汗青堂丛书071·洪水与饥荒:1938至1950年河南黄泛区的战争与生态
穆盛博;亓民帅;林炫羽
统计学习基础
新民说·贝克德意志史I:皇帝、改革者与政治家(全7册)
[德]马提亚斯·贝歇尔 著;任伊乐 译
统计学习基础
我们这一帮(菲利普·罗斯全集)
菲利普·罗斯 著
统计学习基础
多元宇宙是什么关于宇宙起源的新故事
亚历克斯·维连金 著