Foundations of machine learning / Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.

Format:

Book

Author/Creator:

Mohri, Mehryar.

Contributor:

Rostamizadeh, Afshin.

Talwalkar, Ameet

Alumni and Friends Memorial Book Fund.

Series:

Adaptive computation and machine learning series.

Adaptive computation and machine learning series

Language:

English

Subjects (All):

Machine learning.

Computer algorithms.

Physical Description:

xii, 414 pages : illustrations, ; 23cm.

Place of Publication:

Cambridge, MA : MIT Press, [2012]

Summary:

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modem algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.

Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.

The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar. Book jacket.

Contents:

1 Introduction 1

1.1 Applications and problems 1

1.2 Definitions and terminology 3

1.3 Cross-validation 5

1.4 Learning scenarios 7

1.5 Outline 8

2 The PAC Learning Framework 11

2.1 The PAC learning model 11

2.2 Guarantees for finite hypothesis sets - consistent case 17

2.3 Guarantees for finite hypothesis sets - inconsistent case 21

2.4 Generalities 24

2.4.1 Deterministic versus stochastic scenarios 24

2.4.2 Bayes error and noise 25

2.4.3 Estimation and approximation errors 26

2.4.4 Model selection 27

2.5 Chapter notes 28

2.6 Exercises 29

3 Rademacher Complexity and VC-Dimension 33

3.1 Rademacher complexity 34

3.2 Growth function 38

3.3 VC-dimension 41

3.4 Lower bounds 48

3.5 Chapter notes 54

3.6 Exercises 55

4 Support Vector Machines 63

4.1 Linear classification 63

4.2 SVMs - separable case 64

4.2.1 Primal optimization problem 64

4.2.2 Support vectors 66

4.2.3 Dual optimization problem 67

4.2.4 Leave-one-out analysis 69

4.3 SVMs - non-separable case 71

4.3.1 Primal optimization problem 72

4.3.2 Support vectors 73

4.3.3 Dual optimization problem 74

4.4 Margin theory 75

4.5 Chapter notes 83

4.6 Exercises 84

5 Kernel Methods 89

5.1 Introduction 89

5.2 Positive definite symmetric kernels 92

5.2.1 Definitions 92

5.2.2 Reproducing kernel Hilbert space 94

5.2.3 Properties 96

5.3 Kernel-based algorithms 100

5.3.1 SVMs with PDS kernels 100

5.3.2 Representer theorem 101

5.3.3 Learning guarantees 102

5.4 Negative definite symmetric kernels 103

5.5 Sequence kernels 106

5.5.1 Weighted transducers 106

5.5.2 Rational kernels 111

5.6 Chapter notes 115

5.7 Exercises 116

6 Boosting 121

6.1 Introduction 121

6.2 AdaBoost 122

6.2.1 Bound on the empirical error 124

6.2.2 Relationship with coordinate descent 126

6.2.3 Relationship with logistic regression 129

6.2.4 Standard use in practice 129

6.3 Theoretical results 130

6.3.1 VC-dimension-based analysis 131

6.3.2 Margin-based analysis 131

6.3.3 Margin maximization 136

6.3.4 Game-theoretic interpretation 137

6.4 Discussion 140

6.5 Chapter notes 141

6.6 Exercises 142

7 On-Line Learning 147

7.1 Introduction 147

7.2 Prediction with expert advice 148

7.2.1 Mistake bounds and Halving algorithm 148

7.2.2 Weighted majority algorithm 150

7.2.3 Randomized weighted majority algorithm 152

7.2.4 Exponential weighted average algorithm 156

7.3 Linear classification 159

7.3.1 Perceptron algorithm 160

7.3.2 Winnow algorithm 168

7.4 On-line to batch conversion 171

7.5 Game-theoretic connection 174

7.6 Chapter notes 175

7.7 Exercises 176

8 Multi-Class Classification 183

8.1 Multi-class classification problem 183

8.2 Generalization bounds 185

8.3 Uncombined multi-class algorithms 191

8.3.1 Multi-class SVMs 191

8.3.2 Multi-class boosting algorithms 192

8.3.3 Decision trees 194

8.4 Aggregated multi-class algorithms 198

8.4.1 One-versus-all 198

8.4.2 One-versus-one 199

8.4.3 Error-correction codes 201

8.5 Structured prediction algorithms 203

8.6 Chapter notes 206

8.7 Exercises 207

9 Ranking 209

9.1 The problem of ranking 209

9.2 Generalization bound 211

9.3 Ranking with SVMs 213

9.4 RankBoost 214

9.4.1 Bound on the empirical error 216

9.4.2 Relationship with coordinate descent 218

9.4.3 Margin bound for ensemble methods in ranking 220

9.5 Bipartite ranking 221

9.5.1 Boosting in bipartite ranking 222

9.5.2 Area under the ROC curve 224

9.6 Preference-based setting 226

9.6.1 Second-stage ranking problem 227

9.6.2 Deterministic algorithm 229

9.6.3 Randomized algorithm 230

9.6.4 Extension to other loss functions 231

9.7 Discussion 232

9.8 Chapter notes 233

9.9 Exercises 234

10 Regression 237

10.1 The problem of regression 237

10.2 Generalization bounds 238

10.2.1 Finite hypothesis sets 238

10.2.2 Rademacher complexity bounds 239

10.2.3 Pseudo-dimension bounds 241

10.3 Regression algorithms 245

10.3.1 Linear regression 245

10.3.2 Kernel ridge regression 247

10.3.3 Support vector regression 252

10.3.4 Lasso 257

10.3.5 Group norm regression algorithms 260

10.3.6 On-line regression algorithms 261

10.4 Chapter notes 262

10.5 Exercises 263

11 Algorithmic Stability 267

11.1 Definitions 267

11.2 Stability-based generalization guarantee 268

11.3 Stability of kernel-based regularization algorithms 270

11.3.1 Application to regression algorithms: SVR and KRR 274

11.3.2 Application to classification algorithms: SVMs 276

11.3.3 Discussion 276

11.4 Chapter notes 277

11.5 Exercises 277

12 Dimensionality Reduction 281

12.1 Principal Component Analysis 282

12.2 Kernel Principal Component Analysis (KPCA) 283

12.3 KPCA and manifold learning 285

12.3.1 Isomap 285

12.3.2 Laplacian eigenmaps 286

12.3.3 Locally linear embedding (LLE) 287

12.4 Johnson-Lindenstrauss lemma 288

12.5 Chapter notes 290

12.6 Exercises 290

13 Learning Automata and Languages 293

13.1 Introduction 293

13.2 Finite automata 294

13.3 Efficient exact learning 295

13.3.1 Passive learning 296

13.3.2 Learning with queries 297

13.3.3 Learning automata with queries 298

13.4 Identification in the limit 303

13.4.1 Learning reversible automata 304

13.5 Chapter notes 309

13.6 Exercises 310

14 Reinforcement Learning 313

14.1 Learning scenario 313

14.2 Markov decision process model 314

14.3 Policy 315

14.3.1 Definition 315

14.3.2 Policy value 316

14.3.3 Policy evaluation 316

14.3.4 Optimal policy 318

14.4 Planning algorithms 319

14.4.1 Value iteration 319

14.4.2 Policy iteration 322

14.4.3 Linear programming 324

14.5 Learning algorithms 325

14.5.1 Stochastic approximation 326

14.5.2 TD(0) algorithm 330

14.5.3 Q-learning algorithm 331

14.5.4 SARSA 334

14.5.5 TD(λ) algorithm 335

14.5.6 Large state space 336

14.6 Chapter notes 337.

Notes:

Includes bibliographical references and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Alumni and Friends Memorial Book Fund.

ISBN:

9780262018258

026201825X

OCLC:

782251805