Statistical Pattern Recognition
3. Auflage Oktober 2011
666 Seiten, Softcover
Wiley & Sons Ltd
Kurzbeschreibung
Updated to cover the latest developments in statistical pattern recognition, this Third Edition of Statistical Pattern Recognition provides an introduction to the theory and techniques, with material drawn from a wide range of fields. The book describes techniques for analyzing data comprising measurements made on individuals or objects, with emphasis placed on techniques for classification. New material presents practitioners and researchers with the analysis of complex networks and basic techniques for analyzing the properties of datasets, the use of variational methods for Bayesian density estimation, and new applications in biometrics and security.
Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.
This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.
Statistical Pattern Recognition, 3rd Edition:
* Provides a self-contained introduction to statistical pattern recognition.
* Includes new material presenting the analysis of complex networks.
* Introduces readers to methods for Bayesian density estimation.
* Presents descriptions of new applications in biometrics, security, finance and condition monitoring.
* Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications
* Describes mathematically the range of statistical pattern recognition techniques.
* Presents a variety of exercises including more extensive computer projects.
The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.
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Notation xxiii
1 Introduction to Statistical Pattern Recognition 1
1.1 Statistical Pattern Recognition 1
1.2 Stages in a Pattern Recognition Problem 4
1.3 Issues 6
1.4 Approaches to Statistical Pattern Recognition 7
1.5 Elementary Decision Theory 8
1.6 Discriminant Functions 20
1.7 Multiple Regression 27
1.8 Outline of Book 29
1.9 Notes and References 29
Exercises 31
2 Density Estimation - Parametric 33
2.1 Introduction 33
2.2 Estimating the Parameters of the Distributions 34
2.3 The Gaussian Classifier 35
2.4 Dealing with Singularities in the Gaussian Classifier 40
2.5 Finite Mixture Models 46
2.6 Application Studies 63
2.7 Summary and Discussion 66
2.8 Recommendations 66
2.9 Notes and References 67
Exercises 67
3 Density Estimation - Bayesian 70
3.1 Introduction 70
3.2 Analytic Solutions 73
3.3 Bayesian Sampling Schemes 87
3.4 Markov Chain Monte Carlo Methods 95
3.5 Bayesian Approaches to Discrimination 116
3.6 Sequential Monte Carlo Samplers 119
3.7 Variational Bayes 126
3.8 Approximate Bayesian Computation 137
3.9 Example Application Study 144
3.10 Application Studies 145
3.11 Summary and Discussion 146
3.12 Recommendations 147
3.13 Notes and References 147
Exercises 148
4 Density Estimation - Nonparametric 150
4.1 Introduction 150
4.2 k-Nearest-Neighbour Method 152
4.3 Histogram Method 180
4.4 Kernel Methods 194
4.5 Expansion by Basis Functions 204
4.6 Copulas 207
4.7 Application Studies 213
4.8 Summary and Discussion 216
4.9 Recommendations 217
4.10 Notes and References 217
Exercises 218
5 Linear Discriminant Analysis 221
5.1 Introduction 221
5.2 Two-Class Algorithms 222
5.3 Multiclass Algorithms 236
5.4 Support Vector Machines 249
5.5 Logistic Discrimination 263
5.6 Application Studies 268
5.7 Summary and Discussion 268
5.8 Recommendations 269
5.9 Notes and References 270
Exercises 270
6 Nonlinear Discriminant Analysis - Kernel and Projection Methods 274
6.1 Introduction 274
6.2 Radial Basis Functions 276
6.3 Nonlinear Support Vector Machines 291
6.4 The Multilayer Perceptron 298
6.5 Application Studies 314
6.6 Summary and Discussion 316
6.7 Recommendations 317
6.8 Notes and References 318
Exercises 318
7 Rule and Decision Tree Induction 322
7.1 Introduction 322
7.2 Decision Trees 323
7.3 Rule Induction 342
7.4 Multivariate Adaptive Regression Splines 351
7.5 Application Studies 356
7.6 Summary and Discussion 358
7.7 Recommendations 358
7.8 Notes and References 359
Exercises 359
8 Ensemble Methods 361
8.1 Introduction 361
8.2 Characterising a Classifier Combination Scheme 362
8.3 Data Fusion 370
8.4 Classifier Combination Methods 376
8.5 Application Studies 399
8.6 Summary and Discussion 400
8.7 Recommendations 401
8.8 Notes and References 401
Exercises 402
9 Performance Assessment 404
9.1 Introduction 404
9.2 Performance Assessment 405
9.3 Comparing Classifier Performance 424
9.4 Application Studies 429
9.5 Summary and Discussion 430
9.6 Recommendations 430
9.7 Notes and References 430
Exercises 431
10 Feature Selection and Extraction 433
10.1 Introduction 433
10.2 Feature Selection 435
10.3 Linear Feature Extraction 463
10.4 Multidimensional Scaling 484
10.5 Application Studies 493
10.6 Summary and Discussion 495
10.7 Recommendations 495
10.8 Notes and References 496
Exercises 497
11 Clustering 501
11.1 Introduction 501
11.2 Hierarchical Methods 502
11.3 Quick Partitions 510
11.4 Mixture Models 511
11.5 Sum-of-Squares Methods 513
11.6 Spectral Clustering 531
11.7 Cluster Validity 538
11.8 Application Studies 546
11.9 Summary and Discussion 549
11.10 Recommendations 551
11.11 Notes and References 552
Exercises 553
12 Complex Networks 555
12.1 Introduction 555
12.2 Mathematics of Networks 561
12.3 Community Detection 565
12.4 Link Prediction 575
12.5 Application Studies 579
12.6 Summary and Discussion 579
12.7 Recommendations 580
12.8 Notes and References 580
Exercises 580
13 Additional Topics 581
13.1 Model Selection 581
13.2 Missing Data 585
13.3 Outlier Detection and Robust Procedures 586
13.4 Mixed Continuous and Discrete Variables 587
13.5 Structural Risk Minimisation and the Vapnik-Chervonenkis Dimension 588
References 591
Index 637
