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Pattern Recognition and Machine Learning (Information Science and Statistics) (Hardcover)
Christopher M. Bishop (Author)


Editorial Reviews
Review
From the reviews:
"This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software
"In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007)
"This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007)
"Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007)
"The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007)
"This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007)
"Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008)
"This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008)
"Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)
Product Description
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
Coming soon:
*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)
*For instructors, worked solutions to remaining exercises from the Springer web site
*Lecture slides to accompany each chapter
*Data sets available for download

Product Details Hardcover: 738 pages Publisher: Springer; 1st ed. 2006. Corr. 2nd printing edition (October 1, 2007) Language: English ISBN-10: 0387310738 ISBN-13: 978-0387310732

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关键词：Recognition cognition Learning earning Pattern Learning machine Recognition Bishop Pattern

Contents

Preface vii

Mathematical notation xi

Contents xiii

1 Introduction 1

1.1 Example: Polynomial Curve Fitting 4

1.2 Probability Theory 12

1.2.1 Probability densities 17

1.2.2 Expectations and covariances 19

1.2.3 Bayesian probabilities 21

1.2.4 The Gaussian distribution 24

1.2.5 Curve fitting re-visited 28

1.2.6 Bayesian curve fitting 30

1.3 Model Selection 32

1.4 The Curse of Dimensionality 33

1.5 Decision Theory 38

1.5.1 Minimizing the misclassification rate 39

1.5.2 Minimizing the expected loss 41

1.5.3 The reject option 42

1.5.4 Inference and decision 42

1.5.5 Loss functions for regression 46

1.6 Information Theory 48

1.6.1 Relative entropy and mutual information 55

Exercises 58

2 Probability Distributions 67

2.1 Binary Variables 68

2.1.1 The beta distribution 71

2.2 Multinomial Variables 74

2.2.1 The Dirichlet distribution 76

2.3 The Gaussian Distribution 78

2.3.1 Conditional Gaussian distributions 85

2.3.2 Marginal Gaussian distributions 88

2.3.3 Bayes’ theorem for Gaussian variables 90

2.3.4 Maximum likelihood for the Gaussian 93

2.3.5 Sequential estimation 94

2.3.6 Bayesian inference for the Gaussian 97

2.3.7 Student’s t-distribution 102

2.3.8 Periodic variables 105

2.3.9 Mixtures of Gaussians 110

2.4 The Exponential Family 113

2.4.1 Maximum likelihood and sufficient statistics 116

2.4.2 Conjugate priors 117

2.4.3 Noninformative priors 117

2.5 Nonparametric Methods 120

2.5.1 Kernel density estimators 122

2.5.2 Nearest-neighbour methods 124

Exercises 127

3 Linear Models for Regression 137

3.1 Linear Basis Function Models 138

3.1.1 Maximum likelihood and least squares 140

3.1.2 Geometry of least squares 143

3.1.3 Sequential learning 143

3.1.4 Regularized least squares 144

3.1.5 Multiple outputs 146

3.2 The Bias-Variance Decomposition 147

3.3 Bayesian Linear Regression 152

3.3.1 Parameter distribution 152

3.3.2 Predictive distribution 156

3.3.3 Equivalent kernel 159

3.4 Bayesian Model Comparison 161

3.5 The Evidence Approximation 165

3.5.1 Evaluation of the evidence function 166

3.5.2 Maximizing the evidence function 168

3.5.3 Effective number of parameters 170

3.6 Limitations of Fixed Basis Functions 172

Exercises 173

4 Linear Models for Classification 179

4.1 Discriminant Functions 181

4.1.1 Two classes 181

4.1.2 Multiple classes 182

4.1.3 Least squares for classification 184

4.1.4 Fisher’s linear discriminant 186

4.1.5 Relation to least squares 189

4.1.6 Fisher’s discriminant for multiple classes 191

4.1.7 The perceptron algorithm 192

4.2 Probabilistic Generative Models 196

4.2.1 Continuous inputs 198

4.2.2 Maximum likelihood solution 200

4.2.3 Discrete features 202

4.2.4 Exponential family 202

4.3 Probabilistic Discriminative Models 203

4.3.1 Fixed basis functions 204

4.3.2 Logistic regression 205

4.3.3 Iterative reweighted least squares 207

4.3.4 Multiclass logistic regression 209

4.3.5 Probit regression 210

4.3.6 Canonical link functions 212

4.4 The Laplace Approximation 213

4.4.1 Model comparison and BIC 216

4.5 Bayesian Logistic Regression 217

4.5.1 Laplace approximation 217

4.5.2 Predictive distribution 218

Exercises 220

5 Neural Networks 225

5.1 Feed-forward Network Functions 227

5.1.1 Weight-space symmetries 231

5.2 Network Training 232

5.2.1 Parameter optimization 236

5.2.2 Local quadratic approximation 237

5.2.3 Use of gradient information 239

5.2.4 Gradient descent optimization 240

5.3 Error Backpropagation 241

5.3.1 Evaluation of error-function derivatives 242

5.3.2 A simple example 245

5.3.3 Efficiency of backpropagation 246

5.3.4 The Jacobian matrix 247

5.4 The Hessian Matrix 249

5.4.1 Diagonal approximation 250

5.4.2 Outer product approximation 251

5.4.3 Inverse Hessian 252

5.4.4 Finite differences 252

5.4.5 Exact evaluation of the Hessian 253

5.4.6 Fast multiplication by the Hessian 254

5.5 Regularization in Neural Networks 256

5.5.1 Consistent Gaussian priors 257

5.5.2 Early stopping 259

5.5.3 Invariances 261

5.5.4 Tangent propagation 263

5.5.5 Training with transformed data 265

5.5.6 Convolutional networks 267

5.5.7 Soft weight sharing 269

5.6 Mixture Density Networks 272

5.7 Bayesian Neural Networks 277

5.7.1 Posterior parameter distribution 278

5.7.2 Hyperparameter optimization 280

5.7.3 Bayesian neural networks for classification 281

Exercises 284

6 Kernel Methods 291

6.1 Dual Representations 293

6.2 Constructing Kernels 294

6.3 Radial Basis Function Networks 299

6.3.1 Nadaraya-Watson model 301

6.4 Gaussian Processes 303

6.4.1 Linear regression revisited 304

6.4.2 Gaussian processes for regression 306

6.4.3 Learning the hyperparameters 311

6.4.4 Automatic relevance determination 312

6.4.5 Gaussian processes for classification 313

6.4.6 Laplace approximation 315

6.4.7 Connection to neural networks 319

Exercises 320

7 Sparse Kernel Machines 325

7.1 Maximum Margin Classifiers 326

7.1.1 Overlapping class distributions 331

7.1.2 Relation to logistic regression 336

7.1.3 Multiclass SVMs 338

7.1.4 SVMs for regression 339

7.1.5 Computational learning theory 344

7.2 Relevance Vector Machines 345

7.2.1 RVM for regression 345

7.2.2 Analysis of sparsity 349

7.2.3 RVM for classification 353

Exercises 357

8 Graphical Models 359

8.1 Bayesian Networks 360

8.1.1 Example: Polynomial regression 362

8.1.2 Generative models 365

8.1.3 Discrete variables 366

8.1.4 Linear-Gaussian models 370

8.2 Conditional Independence 372

8.2.1 Three example graphs 373

8.2.2 D-separation 378

8.3 Markov Random Fields 383

8.3.1 Conditional independence properties 383

8.3.2 Factorization properties 384

8.3.3 Illustration: Image de-noising 387

8.3.4 Relation to directed graphs 390

8.4 Inference in Graphical Models 393

8.4.1 Inference on a chain 394

8.4.2 Trees 398

8.4.3 Factor graphs 399

8.4.4 The sum-product algorithm 402

8.4.5 The max-sum algorithm 411

8.4.6 Exact inference in general graphs 416

8.4.7 Loopy belief propagation 417

8.4.8 Learning the graph structure 418

Exercises 418

9 Mixture Models and EM 423

9.1 K-means Clustering 424

9.1.1 Image segmentation and compression 428

9.2 Mixtures of Gaussians 430

9.2.1 Maximum likelihood 432

9.2.2 EM for Gaussian mixtures 435

9.3 An Alternative View of EM 439

9.3.1 Gaussian mixtures revisited 441

9.3.2 Relation to K-means 443

9.3.3 Mixtures of Bernoulli distributions 444

9.3.4 EM for Bayesian linear regression 448

9.4 The EM Algorithm in General 450

Exercises 455

10 Approximate Inference 461

10.1 Variational Inference 462

10.1.1 Factorized distributions 464

10.1.2 Properties of factorized approximations 466

10.1.3 Example: The univariate Gaussian 470

10.1.4 Model comparison 473

10.2 Illustration: Variational Mixture of Gaussians 474

10.2.1 Variational distribution 475

10.2.2 Variational lower bound 481

10.2.3 Predictive density 482

10.2.4 Determining the number of components 483

10.2.5 Induced factorizations 485

10.3 Variational Linear Regression 486

10.3.1 Variational distribution 486

10.3.2 Predictive distribution 488

10.3.3 Lower bound 489

10.4 Exponential Family Distributions 490

10.4.1 Variational message passing 491

10.5 Local Variational Methods 493

10.6 Variational Logistic Regression 498

10.6.1 Variational posterior distribution 498

10.6.2 Optimizing the variational parameters 500

10.6.3 Inference of hyperparameters 502

10.7 Expectation Propagation 505

10.7.1 Example: The clutter problem 511

10.7.2 Expectation propagation on graphs 513

Exercises 517

11 Sampling Methods 523

11.1 Basic Sampling Algorithms 526

11.1.1 Standard distributions 526

11.1.2 Rejection sampling 528

11.1.3 Adaptive rejection sampling 530

11.1.4 Importance sampling 532

11.1.5 Sampling-importance-resampling 534

11.1.6 Sampling and the EM algorithm 536

11.2 Markov Chain Monte Carlo 537

11.2.1 Markov chains 539

11.2.2 The Metropolis-Hastings algorithm 541

11.3 Gibbs Sampling 542

11.4 Slice Sampling 546

11.5 The Hybrid Monte Carlo Algorithm 548

11.5.1 Dynamical systems 548

11.5.2 Hybrid Monte Carlo 552

11.6 Estimating the Partition Function 554

Exercises 556

12 Continuous Latent Variables 559

12.1 Principal Component Analysis 561

12.1.1 Maximum variance formulation 561

12.1.2 Minimum-error formulation 563

12.1.3 Applications of PCA 565

12.1.4 PCA for high-dimensional data 569

12.2 Probabilistic PCA 570

12.2.1 Maximum likelihood PCA 574

12.2.2 EM algorithm for PCA 577

12.2.3 Bayesian PCA 580

12.2.4 Factor analysis 583

12.3 Kernel PCA 586

12.4 Nonlinear Latent Variable Models 591

12.4.1 Independent component analysis 591

12.4.2 Autoassociative neural networks 592

12.4.3 Modelling nonlinear manifolds 595

Exercises 599

13 Sequential Data 605

13.1 Markov Models 607

13.2 Hidden Markov Models 610

13.2.1 Maximum likelihood for the HMM 615

13.2.2 The forward-backward algorithm 618

13.2.3 The sum-product algorithm for the HMM 625

13.2.4 Scaling factors 627

13.2.5 The Viterbi algorithm 629

13.2.6 Extensions of the hidden Markov model 631

13.3 Linear Dynamical Systems 635

13.3.1 Inference in LDS 638

13.3.2 Learning in LDS 642

13.3.3 Extensions of LDS 644

13.3.4 Particle filters 645

Exercises 646

14 Combining Models 653

14.1 Bayesian Model Averaging 654

14.2 Committees 655

14.3 Boosting 657

14.3.1 Minimizing exponential error 659

14.3.2 Error functions for boosting 661

14.4 Tree-based Models 663

14.5 Conditional Mixture Models 666

14.5.1 Mixtures of linear regression models 667

14.5.2 Mixtures of logistic models 670

14.5.3 Mixtures of experts 672

Exercises 674

Appendix A Data Sets 677

Appendix B Probability Distributions 685

Appendix C Properties of Matrices 695

Appendix D Calculus of Variations 703

Appendix E Lagrange Multipliers 707

References 711

Index 729

thank you so much for the sharing

太敬业了，谢谢

太感谢了！！！