机器学习:贝叶斯和优化方法 (希)西格尔斯·西奥多里蒂斯 机械工业出版社【正版书】 pdf mobi 下载 kindle 115盘 chm 阿里云 rb

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Contents

Preface.iv

Acknowledgments.vv

Notation.vfivi

CHAPTER 1 Introduction .1

1.1 What Machine Learning is About1

1.1.1 Classification.2

1.1.2 Regression3

1.2 Structure and a Road Map of the Book5

References8

CHAPTER 2 Probability and Stochastic Processes 9

2.1 Introduction.10

2.2 Probability and Random Variables.10

2.2.1Probability11

2.2.2Discrete Random Variables12

2.2.3Continuous Random Variables14

2.2.4Meanand Variance15

2.2.5Transformation of Random Variables.17

2.3 Examples of Distributi***18

2.3.1Discrete Variables18

2.3.2Continuous Variables20

2.4 Stochastic Processes29

2.4.1First and Second Order Statistics.30

2.4.2Stationarity and Ergodicity30

2.4.***owerSpectral Density33

2.4.4Autoregressive Models38

2.5 InformationTheory.41

2.5.1Discrete Random Variables42

2.5.2Continuous Random Variables45

2.6 Stochastic Convergence48

Problems49

References51

CHAPTER 3 Learning in Parametric Modeling: Basic Concepts and Directi*** 53

3.1 Introduction.53

3.2 Parameter Estimation: The Deterministic Point of View.54

3.3 Linear Regression.57

3.4 Classification60

3.5 Biased Versus Unbiased Estimation.64

3.5.1 Biased or Unbiased Estimation?65

3.6 The Cramér-Rao Lower Bound67

3.7 Suf?cient Statistic.70

3.8 Regularization.72

3.9 The Bias-Variance Dilemma.77

3.9.1 Mean-Square Error Estimation77

3.9.2 Bias-Variance Tradeoff78

3.10 MamumLikelihoodMethod.82

3.10.1 Linear Regression: The Nonwhite Gaussian Noise Case84

3.11 Bayesian Inference84

3.11.1 The Mamum a Posteriori Probability Estimation Method.88

3.12 Curse of Dimensionality89

3.13 Validation.91

3.14 Expected and Empirical Loss Functi***.93

3.15 Nonparametric Modeling and Estimation.95

Problems.97

References102

CHAPTER4 Mean-quare Error Linear Estimation105

4.1Introduction.105

4.2Mean-Square Error Linear Estimation: The Normal Equati***106

4.2.1The Cost Function Surface107

4.3A Geometric Viewpoint: Orthogonality Condition109

4.4Extensionto Complex-Valued Variables111

4.4.1Widely Linear Complex-Valued Estimation113

4.4.2Optimizing with Respect to Complex-Valued Variables: Wirtinger Calculus116

4.5Linear Filtering.118

4.6MSE Linear Filtering: A Frequency Domain Point of View120

4.7Some Typical Applicati***.124

4.7.1Interference Cancellation124

4.7.2System Identification125

4.7.3Deconvolution: Channel Equalization126

4.8Algorithmic Aspects: The Levinson and the Lattice-Ladder Algorithms132

4.8.1The Lattice-Ladder Scheme.137

4.9Mean-Square Error Estimation of Linear Models.140

4.9.1The Gauss-Markov Theorem143

4.9.2C***trained Linear Estimation:The Beamforming Case145

4.10Time-Varying Statistics: Kalman Filtering148

Problems.154

References158

CHAPTER 5 Stochastic Gradient Descent: The LMS Algorithm and its Family .161

5.1 Introduction.162

5.2 The Steepest Descent Method163

5.3 Application to the Mean-Square Error Cost Function167

5.3.1 The Complex-Valued Case175

5.4 Stochastic Appromation177

5.5 The Least-Mean-Squares Adaptive Algorithm179

5.5.1 Convergence and Steady-State Performanceof the LMS in Stationary Environments.181

5.5.2 Cumulative Loss Bounds186

5.6 The Affine Projection Algorithm.188

5.6.1 The Normalized LMS.193

5.7 The Complex-Valued Case.194

5.8 Relatives of the LMS.196

5.9 Simulation Examples.199

5.10 Adaptive Decision Feedback Equalization202

5.11 The Linearly C***trained LMS204

5.12 Tracking Performance of the LMS in N***tationary Environments.206

5.13 Distributed Learning:The Distributed LMS208

5.13.1Cooperation Strategies.209

5.13.2The Diffusion LMS211

5.13.3 Convergence and Steady-State Performance: Some Highlights218

5.13.4 C***ensus-Based Distributed Schemes.220

5.14 A Case Study:Target Localization222

5.15 Some Concluding Remarks: C***ensus Matrix.223

Problems.224

References227

CHAPTER 6 The Least-Squares Family 233

6.1 Introduction.234

6.2 Least-Squares Linear Regression: A Geometric Perspective.234

6.3 Statistical Properties of the LS Estimator236

***

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