Statistical Pattern Recognition

Informationen zum Autor Dr Andrew Robert Webb , Senior Researcher, QinetiQ Ltd, Malvern, UK. Dr Keith Derek Copsey , Senior Researcher, QinetiQ Ltd, Malvern, UK. Klappentext 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. http://www.wiley.com/go/statistical_pattern_recognition Zusammenfassung 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. Inhaltsverzeichnis Preface xix Notation xxiii 1 Introduction to Statistical Pattern Recognition 1 1.1 Statistical Pattern Recognition 1 1.1.1 Introduction 1 1.1.2 The Basic Model 2 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 1.5.1 Bayes' Decision Rule for Minimum Error 8 1.5.2 Bayes' Decision Rule for Minimum Error - Reject Option 12 1.5.3 Bayes' Decision Rule for Minimum Risk 13 1.5.4 Bayes' Decision Rule for Minimum Risk - Reject Option 15 1.5.5 Neyman-Pearson Decision Rule 15 1.5.6 Minimax Criterion 18 1.5.7 Discussion 19 1.6 Discriminant Functions 20 1.6.1 Introduction 20 1.6.2 Linear Discriminant Functions 21 1.6.3 Piecewise Linear Discriminant Functions 23 1.6.4 Generalised Linear Discriminant Function 24 1.6.5 Summary 26 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

List of contents

Preface xix
 
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

Report

"In the end I must add that this book is so appealing that I often found myself lost in the reading, pausing the overview of the manuscript in order to look more into some presented subject, and not being able to continue until I had finished seeing all about it." ( Zentralblatt MATH , 1 December 2012)

Product details

• Authors: Gavin Cawley, Keith D Copsey, Keith D. Copsey, Keith Dere Copsey, Keith Derek Copsey, Andrew Webb, Andrew R Webb, Andrew R. Webb, Andrew R. (Qinetiq Ltd) Copsey Webb, Andrew R. Copsey Webb, Andrew R./ Copsey Webb, Ar Webb, WEBB ANDREW R COPSEY KEITH DERE
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 31.10.2011
• EAN: 9780470682289
• ISBN: 978-0-470-68228-9
• No. of pages: 672
• Subjects: Natural sciences, medicine, IT, technology > Mathematics; Statistik, Statistics, Mustererkennung, Statistik in den Ingenieurwissenschaften, Engineering Statistics, Rechnergestützte u. graphische Statistik, Computational & Graphical Statistics