统计学习基础(第2版)(英文)(未拆封)
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35
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119
九五品
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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
装帧平装
货号10
上书时间2024-10-31
商品详情
- 品相描述:九五品
图书标准信息
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作者
[德]黑斯蒂(Hastie T.) 著
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出版社
世界图书出版公司
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出版时间
2015-01
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版次
2
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ISBN
9787510084508
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定价
119.00元
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装帧
平装
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开本
24开
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纸张
胶版纸
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页数
745页
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正文语种
英语
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原版书名
The Elements of Statistical Learning
- 【内容简介】
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Thisbookisourattempttobringtogethermanyoftheimportantnewideasinlearning,andexplaintheminastatisticalframework.Whilesomemathematicaldetailsareneeded,weemphasizethemethodsandtheirconceptualunderpinningsratherthantheirtheoreticalproperties.Asaresult,wehopethatthisbookwillappealnotjusttostatisticiansbutalsotoresearchersandpractitionersinawidevarietyoffields.
- 【作者简介】
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作者:(德国)T.黑斯蒂(Trevor Hastie)
- 【目录】
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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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