统计学习基础(第2版)(英文)

统计学习基础(第2版)(英文)
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作者: [德] (Hastie T.)
出版社: 世界图书出版公司
2015-01
版次: 2
ISBN: 9787510084508
定价: 119.00
装帧: 平装
开本: 24开
纸张: 胶版纸
页数: 745页
正文语种: 英语
原版书名: The Elements of Statistical Learning
分类: 社会文化
  •   Thisbookisourattempttobringtogethermanyoftheimportantnewideasinlearning,andexplaintheminastatisticalframework.Whilesomemathematicaldetailsareneeded,weemphasizethemethodsandtheirconceptualunderpinningsratherthantheirtheoreticalproperties.Asaresult,wehopethatthisbookwillappealnotjusttostatisticiansbutalsotoresearchersandpractitionersinawidevarietyoffields. 作者:(德国)T.黑斯蒂(Trevor Hastie) PrefacetotheSecondEdition
    PrefacetotheFirstEdition
    1Introduction

    2OverviewofSupervisedLearning
    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-Variancerlyadeoff
    BibliographicNotes
    Exercises

    3LinearMethodsforRegression
    3.1Introduction
    3.2LinearRegressionModelsandLeastSquares
    3.2.1Example:ProstateCancer
    3.2.2TheGauss-MarkovTheorem
    3.2.3MultipleRegressionfromSimpleUnivariateRegression
    3.2.4MultipleOutputs
    3.3SubsetSelection
    3.3.1Best-SubsetSelection
    3.3.2Forward-andBackward-StepwiseSelection
    3.3.3Forward-StagewiseRegression
    3.3.4ProstateCancerDataExample(Continued)
    3.4ShrinkageMethods
    3.4.1RidgeRegression
    3.4.2TheLasso
    3.4.3Discussion:SubsetSelection,RidgeRegressionandtheLasso
    3.4.4LeastAngleRegression
    3.5MethodsUsingDerivedInputDirections
    3.5.1PrincipalComponentsRegression
    3.5.2PartialLeastSquares
    3.6Discussion:AComparisonoftheSelectionandShrinkageMethods
    3.7MultipleOutcomeShrinkageandSelection
    3.8MoreontheLassoandRelatedPathAlgorithms
    3.8.1IncrementalForwardStagewiseRegression
    3.8.2Piecewise-LinearPathAlgorithms
    3.8.3TheDantzigSelector
    3.8.4TheGroupedLasso
    3.8.5FurtherPropertiesoftheLasso
    3.8.6PathwiseCoordinateOptimization
    3.9ComputationalConsiderations
    BibliographicNotes
    Exercises
    ……
    4LinearMethodsforClassification
    5BasisExpansionsandRegularization
    6KernelSmoothingMethods
    7ModelAssessmentandSelection
    8ModellnferenceandAveraging
    9AdditiveModels,Trees,andRelatedMethods
    10BoostingandAdditiveTrees
    11NeuralNetworks
    12SupportVectorMachinesandFlexibleDiscriminants
    13PrototypeMethodsandNearest-Neighbors
    14UnsupervisedLearning
    15RandomForests
    16EnsembleLearning
    17UndirectedGraphicalModels
    18High-DimensionalProblems:p≥N
    References
    AuthorIndex
    Index
  • 内容简介:
      Thisbookisourattempttobringtogethermanyoftheimportantnewideasinlearning,andexplaintheminastatisticalframework.Whilesomemathematicaldetailsareneeded,weemphasizethemethodsandtheirconceptualunderpinningsratherthantheirtheoreticalproperties.Asaresult,wehopethatthisbookwillappealnotjusttostatisticiansbutalsotoresearchersandpractitionersinawidevarietyoffields.
  • 作者简介:
    作者:(德国)T.黑斯蒂(Trevor Hastie)
  • 目录:
    PrefacetotheSecondEdition
    PrefacetotheFirstEdition
    1Introduction

    2OverviewofSupervisedLearning
    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-Variancerlyadeoff
    BibliographicNotes
    Exercises

    3LinearMethodsforRegression
    3.1Introduction
    3.2LinearRegressionModelsandLeastSquares
    3.2.1Example:ProstateCancer
    3.2.2TheGauss-MarkovTheorem
    3.2.3MultipleRegressionfromSimpleUnivariateRegression
    3.2.4MultipleOutputs
    3.3SubsetSelection
    3.3.1Best-SubsetSelection
    3.3.2Forward-andBackward-StepwiseSelection
    3.3.3Forward-StagewiseRegression
    3.3.4ProstateCancerDataExample(Continued)
    3.4ShrinkageMethods
    3.4.1RidgeRegression
    3.4.2TheLasso
    3.4.3Discussion:SubsetSelection,RidgeRegressionandtheLasso
    3.4.4LeastAngleRegression
    3.5MethodsUsingDerivedInputDirections
    3.5.1PrincipalComponentsRegression
    3.5.2PartialLeastSquares
    3.6Discussion:AComparisonoftheSelectionandShrinkageMethods
    3.7MultipleOutcomeShrinkageandSelection
    3.8MoreontheLassoandRelatedPathAlgorithms
    3.8.1IncrementalForwardStagewiseRegression
    3.8.2Piecewise-LinearPathAlgorithms
    3.8.3TheDantzigSelector
    3.8.4TheGroupedLasso
    3.8.5FurtherPropertiesoftheLasso
    3.8.6PathwiseCoordinateOptimization
    3.9ComputationalConsiderations
    BibliographicNotes
    Exercises
    ……
    4LinearMethodsforClassification
    5BasisExpansionsandRegularization
    6KernelSmoothingMethods
    7ModelAssessmentandSelection
    8ModellnferenceandAveraging
    9AdditiveModels,Trees,andRelatedMethods
    10BoostingandAdditiveTrees
    11NeuralNetworks
    12SupportVectorMachinesandFlexibleDiscriminants
    13PrototypeMethodsandNearest-Neighbors
    14UnsupervisedLearning
    15RandomForests
    16EnsembleLearning
    17UndirectedGraphicalModels
    18High-DimensionalProblems:p≥N
    References
    AuthorIndex
    Index
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