统计学基础
出版社：世界图书出版公司北京公司
年代：2008
定价：88.0
书籍简介:
本书旨在让读者深入了解数据挖掘和预测。目次：导论；监督学习概述；线性回归模型；线性分类方法；基展开与正则性；核方法；模型评估与选择；模型参考与平均可加性模型，树与相关方法；神经网络；支持向量机器与弹性准则；原型法和最近邻居；无监督学习。
书籍目录:
Preface
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-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
10BoostingandAdditiveTrees
11NeuralNetworks
12SupportVectorMachinesandFlexibleDiscriminants
13PrototypeMethodsandNearest-Neighbors
14UnsupervisedLearning
References
AuthorIndex
Index
内容摘要:
Thisbookisourattempttobringtogethermanyoftheimportantnewideasinlearning,andexplaintheminastatisticalframework.Whilesomemathematicaldetailsareneeded,weemphasizethemethodsandtheirconceptualunderpinningsratherthantheirtheoreticalproperties.Asaresult,wehopethatthisbookwillappealnotjusttostatisticiansbutalsotoresearchersandpractitionersinawidevarietyoffields.
书籍规格:
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| 书名 | 统计学基础站内查询相似图书 | ||
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| 出版地 | 北京 | 出版单位 | 世界图书出版公司北京公司 |
| 版次 | 1版 | 印次 | 1 |
| 定价(元) | 88.0 | 语种 | 英文 |
| 尺寸 | 14 | 装帧 | 平装 |
| 页数 | 印数 | 2000 | |
书籍信息归属:
统计学基础是世界图书出版公司北京公司于2008.05出版的中图分类号为 C8 的主题关于 统计学－英文 的书籍。
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