机器学习 mobi 下载 网盘 caj lrf pdf txt 阿里云

本书对所有主要的机器学习方法和新研究趋势进行了深入探索，涵盖概率和确定性方法以及贝叶斯推断方法。其中，经典方法包括平均/小二乘滤波、卡尔曼滤波、随机逼近和在线学习、贝叶斯分类、决策树、逻辑回归和提升方法等，新趋势包括稀疏、凸分析与优化、在线分布式算法、RKH空间学习、贝叶斯推断、图模型与隐马尔可夫模型、粒子滤波、深度学习、字典学习和潜变量建模等。全书构建了一套明晰的机器学习知识体系，各章内容相对***，物理推理、数学建模和算法实现精准且细致，并辅以应用实例和习题。本书适合该领域的科研人员和工程师阅读，也适合学习模式识别、统计/自适应***处理和深度学习等课程的学生参考。

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 MaximumLikelihoodMethod.82

3.10.1 Linear Regression: The Nonwhite Gaussian Noise Case84

3.11 Bayesian Inference84

3.11.1 The Maximum 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 Approximation177

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

***

作者简介

Sergios Theodoridis 希腊雅典大学信息系教授。主要研究方向是自适应***处理、通信与模式识别。他是欧洲并行结构及语言协会（PARLE-95）的主席和欧洲***处理协会（EUSIPCO-98）的常务主席、《***处理》杂志编委。

K***tantinos Koutroumbas 1995年在希腊雅典大学获得博士学位。自2001年起任职于希腊雅典国家天文台空间应用研究院，是国际知名的专家。

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书籍介绍

本书对所有主要的机器学习方法和新研究趋势进行了深入探索，涵盖概率和确定性方法以及贝叶斯推断方法。其中，经典方法包括平均/小二乘滤波、卡尔曼滤波、随机逼近和在线学习、贝叶斯分类、决策树、逻辑回归和提升方法等，新趋势包括稀疏、凸分析与优化、在线分布式算法、RKH空间学习、贝叶斯推断、图模型与隐马尔可夫模型、粒子滤波、深度学习、字典学习和潜变量建模等。全书构建了一套明晰的机器学习知识体系，各章内容相对***，物理推理、数学建模和算法实现精准且细致，并辅以应用实例和习题。本书适合该领域的科研人员和工程师阅读，也适合学习模式识别、统计/自适应***处理和深度学习等课程的学生参考。