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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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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Seitenzahl: 1153
Veröffentlichungsjahr: 2011
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Contents
Cover
Title Page
Copyright
Dedication
Preface
Scope
Approach
New to the third edition
Book outline
Book website
Acknowledgements
Notation
1: Introduction to statistical pattern recognition
1.1 Statistical pattern recognition
1.2 Stages in a pattern recognition problem
1.3 Issues
1.4 Approaches to statistical pattern recognition
1.5 Elementary decision theory
1.6 Discriminant functions
1.7 Multiple regression
1.8 Outline of book
1.9 Notes and references
2: Density estimation – parametric
2.1 Introduction
2.2 Estimating the parameters of the distributions
2.3 The Gaussian classifier
2.4 Dealing with singularities in the Gaussian classifier
2.5 Finite mixture models
2.6 Application studies
2.7 Summary and discussion
2.8 Recommendations
2.9 Notes and references
3: Density estimation – Bayesian
3.1 Introduction
3.2 Analytic solutions
3.3 Bayesian sampling schemes
3.4 Markov chain Monte Carlo methods
3.5 Bayesian approaches to discrimination
3.6 Sequential Monte Carlo samplers
3.7 Variational Bayes
3.8 Approximate Bayesian Computation
3.9 Example application study
3.10 Application studies
3.11 Summary and discussion
3.12 Recommendations
3.13 Notes and references
4: Density estimation – nonparametric
4.1 Introduction
4.2 k-nearest-neighbour method
4.3 Histogram method
4.4 Kernel methods
4.5 Expansion by basis functions
4.6 Copulas
4.7 Application studies
4.8 Summary and discussion
4.9 Recommendations
4.10 Notes and references
5: Linear discriminant analysis
5.1 Introduction
5.2 Two-class algorithms
5.3 Multiclass algorithms
5.4 Support vector machines
5.5 Logistic discrimination
5.6 Application studies
5.7 Summary and discussion
5.8 Recommendations
5.9 Notes and references
6: Nonlinear discriminant analysis – kernel and projection methods
6.1 Introduction
6.2 Radial basis functions
6.3 Nonlinear support vector machines
6.4 The multilayer perceptron
6.5 Application studies
6.6 Summary and discussion
6.7 Recommendations
6.8 Notes and references
7: Rule and decision tree induction
7.1 Introduction
7.2 Decision trees
7.3 Rule induction
7.4 Multivariate adaptive regression splines
7.5 Application studies
7.6 Summary and discussion
7.7 Recommendations
7.8 Notes and references
8: Ensemble methods
8.1 Introduction
8.2 Characterising a classifier combination scheme
8.3 Data fusion
8.4 Classifier combination methods
8.5 Application studies
8.6 Summary and discussion
8.7 Recommendations
8.8 Notes and references
9: Performance assessment
9.1 Introduction
9.2 Performance assessment
9.3 Comparing classifier performance
9.4 Application studies
9.5 Summary and discussion
9.6 Recommendations
9.7 Notes and references
10: Feature selection and extraction
10.1 Introduction
10.2 Feature selection
10.3 Linear feature extraction
10.4 Multidimensional scaling
10.5 Application studies
10.6 Summary and discussion
10.7 Recommendations
10.8 Notes and references
11: Clustering
11.1 Introduction
11.2 Hierarchical methods
11.3 Quick partitions
11.4 Mixture models
11.5 Sum-of-squares methods
11.6 Spectral clustering
11.7 Cluster validity
11.8 Application studies
11.9 Summary and discussion
11.10 Recommendations
11.11 Notes and references
12: Complex networks
12.1 Introduction
12.2 Mathematics of networks
12.3 Community detection
12.4 Link prediction
12.5 Application studies
12.6 Summary and discussion
12.7 Recommendations
12.8 Notes and references
13: Additional topics
13.1 Model selection
13.2 Missing data
13.3 Outlier detection and robust procedures
13.4 Mixed continuous and discrete variables
13.5 Structural risk minimisation and the Vapnik-Chervonenkis dimension
References
Index
This edition first published 2011 © 2011 John Wiley & Sons, Ltd
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Library of Congress Cataloging-in-Publication Data
Webb, A. R. (Andrew R.) Statistical pattern recognition / Andrew R. Webb, Keith D. Copsey. – 3rd ed. p. cm. Includes bibliographical references and index. ISBN 978-0-470-68227-2 (hardback) – ISBN 978-0-470-68228-9 (paper) 1. Pattern perception–Statistical methods. I. Copsey, Keith D. II. Title.
Q327.W43 2011
006.4–dc23
2011024957
A catalogue record for this book is available from the British Library.
HB ISBN: 978-0-470-68227-2 PB ISBN: 978-0-470-68228-9 ePDF ISBN: 978-1-119-95296-1 oBook ISBN: 978-1-119-95295-4 ePub ISBN: 978-1-119-96140-6 Mobi ISBN: 978-1-119-96141-3
To Rosemary, Samuel, Miriam, Jacob and Ethan
Preface
This book provides an introduction to statistical pattern recognition theory and techniques. Most of the material presented in this book is concerned with discrimination and classification and has been drawn from a wide range of literature including that of engineering, statistics, computer science and the social sciences. The aim of the book is to provide descriptions of many of the most useful of today's pattern processing techniques including many of the recent advances in nonparametric approaches to discrimination and Bayesian computational methods developed in the statistics literature and elsewhere. Discussions provided on the motivations and theory behind these techniques will enable the practitioner to gain maximum benefit from their implementations within many of the popular software packages. The techniques are illustrated with examples of real-world applications studies. Pointers are also provided to the diverse literature base where further details on applications, comparative studies and theoretical developments may be obtained.
The book grew out of our research on the development of statistical pattern recognition methodology and its application to practical sensor data analysis problems. The book is aimed at advanced undergraduate and graduate courses. Some of the material has been presented as part of a graduate course on pattern recognition and at pattern recognition summer schools. It is also designed for practitioners in the field of pattern recognition as well as researchers in the area. A prerequisite is a knowledge of basic probability theory and linear algebra, together with basic knowledge of mathematical methods (for example, Lagrange multipliers are used to solve problems with equality and inequality constraints in some derivations). Some basic material (which was provided as appendices in the second edition) is available on the book's website.
Scope
The book presents most of the popular methods of statistical pattern recognition. However, many of the important developments in pattern recognition are not confined to the statistics literature and have occurred where the area overlaps with research in machine learning. Therefore, where we have felt that straying beyond the traditional boundaries of statistical pattern recognition would be beneficial, we have done so. An example is the inclusion of some rule induction methods as a complementary approach to rule discovery by decision tree induction.
Most of the methodology is generic – it is not specific to a particular type of data or application. Thus, we exclude preprocessing methods and filtering methods commonly used in signal and image processing.
Approach
The approach in each chapter has been to introduce some of the basic concepts and algorithms and to conclude each section on a technique or a class of techniques with a practical application of the approach from the literature. The main aim has been to introduce the basic concept of an approach. Sometimes this has required some detailed mathematical description and clearly we have had to draw a line on how much depth we discuss a particular topic. Most of the topics have whole books devoted to them and so we have had to be selective in our choice of material. Therefore, the chapters conclude with a section on the key references. The exercises at the ends of the chapters vary from `open book’ questions to more lengthy computer projects.
New to the third edition
Many sections have been rewritten and new material added. The new features of this edition include the following:
A new chapter on Bayesian approaches to density estimation (Chapter ) including expanded material on Bayesian sampling schemes and Markov chain Monte Carlo methods, and new sections on Sequential Monte Carlo samplers and Variational Bayes approaches.New sections on nonparametric methods of density estimation.Rule induction.New chapter on ensemble methods of classification.Revision of feature selection material with new section on stability.Spectral clustering.New chapter on complex networks, with relevance to the high-growth field of social and computer network analysis.Book outline
Chapter provides an introduction to statistical pattern recognition, defining some terminology, introducing supervised and unsupervised classification. Two related approaches to supervised classification are presented: one based on the use of probability density functions and a second based on the construction of discriminant functions. The chapter concludes with an outline of the pattern recognition cycle, putting the remaining chapters of the book into context. Chapters , and pursue the density function approach to discrimination. Chapter addresses parametric approaches to density estimation, which are developed further in Chapter on Bayesian methods. Chapter develops classifiers based on nonparametric schemes, including the popular k nearest neighbour method, with associated efficient search algorithms.
Chapters 5–7 develop discriminant function approaches to supervised classification. Chapter focuses on linear discriminant functions; much of the methodology of this chapter (including optimisation, regularisation, support vector machines) is used in some of the nonlinear methods described in Chapter which explores kernel-based methods, in particular, the radial basis function network and the support vector machine, and projection-based methods (the multilayer perceptron). These are commonly referred to as neural network methods. Chapter considers approaches to discrimination that enable the classification function to be cast in the form of an interpretable rule, important for some applications.
Chapter considers ensemble methods – combining classifiers for improved robustness. Chapter considers methods of measuring the performance of a classifier.
The techniques of Chapters and may be described as methods of exploratory data analysis or preprocessing (and as such would usually be carried out prior to the supervised classification techniques of Chapters 5–7, although they could, on occasion, be post-processors of supervised techniques). Chapter addresses feature selection and feature extraction – the procedures for obtaining a reduced set of variables characterising the original data. Such procedures are often an integral part of classifier design and it is somewhat artificial to partition the pattern recognition problem into separate processes of feature extraction and classification. However, feature extraction may provide insights into the data structure and the type of classifier to employ; thus, it is of interest in its own right. Chapter considers unsupervised classification or clustering – the process of grouping individuals in a population to discover the presence of structure; its engineering application is to vector quantisation for image and speech coding. Chapter on complex networks introduces methods for analysing data that may be represented using the mathematical concept of a graph. This has great relevance to social and computer networks.
Finally, Chapter addresses some important diverse topics including model selection.
Book website
The website www.wiley.com/go/statistical_pattern_recognition contains supplementary material on topics including measures of dissimilarity, estimation, linear algebra, data analysis and basic probability.
Acknowledgements
In preparing the third edition of this book we have been helped by many people. We are especially grateful to Dr Gavin Cawley, University of East Anglia, for help and advice. We are grateful to friends and colleagues (past and present, from RSRE, DERA and QinetiQ) who have provided encouragement and made comments on various parts of the manuscript. In particular, we would like to thank Anna Skeoch for providing figures for Chapter ; and Richard Davies and colleagues at John Wiley for help in the final production of the manuscript. Andrew Webb is especially thankful to Rosemary for her love, support and patience.
Andrew R. Webb Keith D. Copsey
