统计学习基础(第2版)(英文)

作者: [德] 黑斯蒂（Hastie T.）著

出版社: 世界图书出版公司

出版时间: 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

4 张插图图片

382人买过

　　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