Foundations of Machine Learning

Hardcover (Print)
Used and New from Other Sellers
Used and New from Other Sellers
from $47.04
Usually ships in 1-2 business days
(Save 36%)
Other sellers (Hardcover)
  • All (10) from $47.04   
  • New (10) from $47.04   

Overview

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern 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.

Read More Show Less

Editorial Reviews

From the Publisher

"In my opinion, the content of the book is outstanding in terms of clarity of discourse and the variety of well-selected examples and exercises. The enlightening commentsprovided by the author at the end of each chapter and the suggestions for further reading are also important features of the book. The concepts and methods are presented in a very clear and accessible way and the illustrative examples contribute substantially to facilitating the understanding of the overall work." -- Computing Reviews

The MIT Press

Read More Show Less

Product Details

Meet the Author

Mehryar Mohri is Professor of Computer Science at New York University's Courant Institute ofMathematical Sciences and a Research Consultant at Google Research.

Afshin Rostamizadeh is a Research Scientist at Google Research.

Ameet Talwalkar is a National Science Foundation Postdoctoral Fellow in the Department ofElectrical Engineering and Computer Science at the University of California, Berkeley.

Read More Show Less

Table of Contents

Preface xi

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

Conclusion 339

A Linear Algebra Review 341

A.1 Vectors and norms 341

A.1.1 Norms 341

A.1.2 Dual norms 342

A.2 Matrices 344

A.2.1 Matrix norms 344

A.2.2 Singular value decomposition 345

A.2.3 Symmetric positive semidefinite (SPSD) matrices 346

B Convex Optimization 349

B.1 Differentiation and unconstrained optimization 349

B.2 Convexity 350

B.3 Constrained optimization 353

B.4 Chapter notes 357

C Probability Review 359

C.1 Probability 359

C.2 Random variables 359

C.3 Conditional probability and independence 361

C.4 Expectation, Markov's inequality, and moment-generating function 363

C.5 Variance and Chebyshev's inequality 365

D Concentration inequalities 369

D.1 Hoeffding's inequality 369

D.2 McDiarmid's inequality 371

D.3 Other inequalities 373

D.3.1 Binomial distribution: Slud's inequality 374

D.3.2 Normal distribution: tail bound 374

D.3.3 Khintchine-Kahane inequality 374

D.4 Chapter notes 376

D.5 Exercises 377

E Notation 379

References 381

Index 397

Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)