Statistical Pattern Recognition 3rd Edition by Andrew R Webb ,Keith D Copsey ISBN 0470682272 978-0470682272

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ISBN 10:0470682272 

ISBN 13:978-0470682272

Author:Andrew R.Webb ,Keith D.Copsey

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, 3^rd 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.

Table of contents: 

Introduction to Statistical Pattern Recognition

Statistical Pattern Recognition

Introduction

The Basic Model

Stages in a Pattern Recognition Problem

Issues

Approaches to Statistical Pattern Recognition

Elementary Decision Theory

Bayes’ Decision Rule for Minimum Error

Bayes’ Decision Rule for Minimum Error – Reject Option

Bayes’ Decision Rule for Minimum Risk

Bayes’ Decision Rule for Minimum Risk – Reject Option

Neyman–Pearson Decision Rule

Minimax Criterion

Discussion

Discriminant Functions

Introduction

Linear Discriminant Functions

Piecewise Linear Discriminant Functions

Generalised Linear Discriminant Function

Summary

Multiple Regression

Outline of Book

Notes and References

Exercises

Density Estimation – Parametric

Introduction

Estimating the Parameters of the Distributions

Estimative Approach

Predictive Approach

The Gaussian Classifier

Specification

Derivation of the Gaussian Classifier Plug-In Estimates

Example Application Study

Dealing with Singularities in the Gaussian Classifier

Introduction

Naïve Bayes

Projection onto a Subspace

Linear Discriminant Function

Regularised Discriminant Analysis

Example Application Study

Further Developments

Summary

Finite Mixture Models

Introduction

Mixture Models for Discrimination

Parameter Estimation for Normal Mixture Models

Normal Mixture Model Covariance Matrix Constraints

How Many Components?

Maximum Likelihood Estimation via EM

Example Application Study

Further Developments

Summary

Application Studies

Summary and Discussion

Recommendations

Notes and References

Exercises

Density Estimation – Bayesian

Introduction

Basics

Recursive Calculation

Proportionality

Analytic Solutions

Conjugate Priors

Estimating the Mean of a Normal Distribution with Known Variance

Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution

Unknown Prior Class Probabilities

Summary

Bayesian Sampling Schemes

Introduction

Summarisation

Sampling Version of the Bayesian Classifier

Rejection Sampling

Ratio of Uniforms

Importance Sampling

Markov Chain Monte Carlo Methods

Introduction

The Gibbs Sampler

Metropolis–Hastings Algorithm

Data Augmentation

Reversible Jump Markov Chain Monte Carlo

Slice Sampling

MCMC Example – Estimation of Noisy Sinusoids

Summary

Notes and References

Bayesian Approaches to Discrimination

Labelled Training Data

Unlabelled Training Data

Sequential Monte Carlo Samplers

Introduction

Basic Methodology

Summary

Variational Bayes

Introduction

Description

Factorised Variational Approximation

Simple Example

Use of the Procedure for Model Selection

Further Developments and Applications

Summary

Approximate Bayesian Computation

Introduction

ABC Rejection Sampling

ABC MCMC Sampling

ABC Population Monte Carlo Sampling

Model Selection

Summary

Example Application Study

Application Studies

Summary and Discussion

Recommendations

Notes and References

Density Estimation – Nonparametric

1. Introduction

2. Basic Properties of Density Estimators

3. k-Nearest-Neighbour Method

4. k-Nearest-Neighbour Classifier

5. Derivation

6. Choice of Distance Metric

7. Properties of the Nearest-Neighbour Rule

8. Linear Approximating and Eliminating Search Algorithm

9. Branch and Bound Search Algorithms: kd-Trees

10. Branch and Bound Search Algorithms: Ball-Trees

11. Editing Techniques

12. Example Application Study

13. Further Developments

14. Summary

15. Histogram Method

16. Data Adaptive Histograms

17. Independence Assumption (Naïve Bayes)

18. Lancaster Models

19. Maximum Weight Dependence Trees

20. Bayesian Networks

21. Example Application Study – Naïve Bayes Text Classification

22. Summary

23. Kernel Methods

24. Biasedness

25. Multivariate Extension

26. Choice of Smoothing Parameter

27. Choice of Kernel

28. Example Application Study

29. Further Developments

30. Summary

31. Expansion by Basis Functions

32. Copulas

33. Introduction

34. Mathematical Basis

35. Copula Functions

36. Estimating Copula Probability Density Functions

37. Simple Example

38. Summary

39. Application Studies

40. Comparative Studies

41. Summary and Discussion

42. Recommendations

43. Notes and References

44. Exercises

Linear Discriminant Analysis

1. Introduction

2. Two-Class Algorithms

3. General Ideas

4. Perceptron Criterion

5. Fisher’s Criterion

6. Least Mean-Squared-Error Procedures

7. Further Developments

8. Summary

9. Multiclass Algorithms

10. General Ideas

11. Error-Correction Procedure

12. Fisher’s Criterion – Linear Discriminant Analysis

13. Least Mean-Squared-Error Procedures

14. Regularisation

15. Example Application Study

16. Further Developments

17. Summary

18. Support Vector Machines

19. Introduction

20. Linearly Separable Two-Class Data

21. Linearly Nonseparable Two-Class Data

22. Multiclass SVMs

23. SVMs for Regression

24. Implementation

25. Example Application Study

26. Summary

27. Logistic Discrimination

28. Two-Class Case

29. Maximum Likelihood Estimation

30. Multiclass Logistic Discrimination

31. Example Application Study

32. Further Developments

33. Summary

34. Application Studies

35. Summary and Discussion

36. Recommendations

37. Notes and References

38. Exercises

Nonlinear Discriminant Analysis – Kernel and Projection Methods

1. Introduction

2. Radial Basis Functions

3. Introduction

4. Specifying the Model

5. Specifying the Functional Form

6. The Positions of the Centres

7. Smoothing Parameters

8. Calculation of the Weights

9. Model Order Selection

10. Simple RBF

11. Motivation

12. RBF Properties

13. Example Application Study

14. Further Developments

15. Summary

16. Nonlinear Support Vector Machines

17. Introduction

18. Binary Classification

19. Types of Kernel

20. Model Selection

21. Multiclass SVMs

22. Probability Estimates

23. Nonlinear Regression

24. Example Application Study

25. Further Developments

26. Summary

27. The Multilayer Perceptron

28. Introduction

29. Specifying the MLP Structure

30. Determining the MLP Weights

31. Modelling Capacity of the MLP

32. Logistic Classification

33. Example Application Study

34. Bayesian MLP Networks

35. Projection Pursuit

36. Summary

37. Application Studies

38. Summary and Discussion

39. Recommendations

40. Notes and References

41. Exercises

Rule and Decision Tree Induction

1. Introduction

2. Decision Trees

3. Introduction

4. Decision Tree Construction

5. Selection of the Splitting Rule

6. Terminating the Splitting Procedure

7. Assigning Class Labels to Terminal Nodes

8. Decision Tree Pruning – Worked Example

9. Decision Tree Construction Methods

10. Other Issues

11. Example Application Study

12. Further Developments

13. Summary

14. Rule Induction

15. Introduction

16. Generating Rules from a Decision Tree

17. Rule Induction Using a Sequential Covering Algorithm

18. Example Application Study

19. Further Developments

20. Summary

21. Multivariate Adaptive Regression Splines

22. Introduction

23. Recursive Partitioning Model

24. Example Application Study

25. Further Developments

26. Summary

27. Application Studies

28. Summary and Discussion

29. Recommendations

30. Notes and References

31. Exercises

Ensemble Methods

1. Introduction

2. Characterising a Classifier Combination Scheme

3. Feature Space

4. Level

5. Degree of Training

6. Form of Component Classifiers

7. Structure

8. Optimisation

9. Data Fusion

10. Architectures

11. Bayesian Approaches

12. Neyman–Pearson Formulation

13. Trainable Rules

14. Fixed Rules

15. Classifier Combination Methods

16. Product Rule

17. Sum Rule

18. Min, Max and Median Combiners

19. Majority Vote

20. Borda Count

21. Combiners Trained on Class Predictions

22. Stacked Generalisation

23. Mixture of Experts

24. Bagging

25. Boosting

26. Random Forests

27. Model Averaging

28. Summary of Methods

29. Example Application Study

30. Further Developments

31. Application Studies

32. Summary and Discussion

33. Recommendations

34. Notes and References

35. Exercises

Performance Assessment

1. Introduction

2. Performance Assessment

3. Performance Measures

4. Discriminability

5. Reliability

6. ROC Curves for Performance Assessment

7. Population and Sensor Drift

8. Example Application Study

9. Further Developments

10. Summary

11. Comparing Classifier Performance

12. Which Technique is Best?

13. Statistical Tests

14. Comparing Rules When Misclassification Costs are Uncertain

15. Example Application Study

16. Further Developments

17. Summary

18. Application Studies

19. Summary and Discussion

20. Recommendations

21. Notes and References

22. Exercises

Feature Selection and Extraction

1. Introduction

2. Feature Selection

3. Introduction

4. Characterisation of Feature Selection Approaches

5. Evaluation Measures

6. Search Algorithms for Feature Subset Selection

7. Complete Search – Branch and Bound

8. Sequential Search

9. Random Search

10. Markov Blanket

11. Stability of Feature Selection

12. Example Application Study

13. Further Developments

14. Summary

15. Linear Feature Extraction

16. Principal Components Analysis

17. Karhunen–Loeve Transformation

18. Example Application Study

19. Further Developments

20. Summary

21. Multidimensional Scaling

22. Classical Scaling

23. Metric MDS

24. Ordinal Scaling

25. Algorithms

26. MDS for Feature Extraction

27. Example Application Study

28. Further Developments

29. Summary

30. Application Studies

31. Summary and Discussion

32. Recommendations

33. Notes and References

34. Exercises

Clustering

1. Introduction

2. Hierarchical Methods

3. Single-Link Method

4. Complete-Link Method

5. Sum-of-Squares Method

6. General Agglomerative Algorithm

7. Properties of a Hierarchical Classification

8. Example Application Study

9. Summary

10. Quick Partitions

11. Mixture Models

12. Model Description

13. Example Application Study

14. Sum-of-Squares Methods

15. Clustering Criteria

16. Clustering Algorithms

17. Vector Quantisation

18. Example Application Study

19. Further Developments

20. Summary

21. Spectral Clustering

22. Elementary Graph Theory

23. Similarity Matrices

24. Application to Clustering

25. Spectral Clustering Algorithm

26. Forms of Graph Laplacian

27. Example Application Study

28. Further Developments

29. Summary

30. Cluster Validity

31. Introduction

32. Statistical Tests

33. Absence of Class Structure

34. Validity of Individual Clusters

35. Hierarchical Clustering

36. Validation of Individual Clusterings

37. Partitions

38. Relative Criteria

39. Choosing the Number of Clusters

40. Application Studies

41. Summary and Discussion

42. Recommendations

43. Notes and References

44. Exercises

Complex Networks

1. Introduction

2. Characteristics

3. Properties

4. Questions to Address

5. Descriptive Features

6. Outline

7. Mathematics of Networks

8. Graph Matrices

9. Connectivity

10. Distance Measures

11. Weighted Networks

12. Centrality Measures

13. Random Graphs

14. Community Detection

15. Clustering Methods

16. Girvan–Newman Algorithm

17. Modularity Approaches

18. Local Modularity

19. Clique Percolation

20. Example Application Study

21. Further Developments

22. Summary

23. Link Prediction

24. Approaches to Link Prediction

25. Example Application Study

26. Further Developments

27. Application Studies

28. Summary and Discussion

29. Recommendations

30. Notes and References

31. Exercises

Additional Topics

Model Selection

Separate Training and Test Sets

Cross-Validation

The Bayesian Viewpoint

Akaike’s Information Criterion

Minimum Description Length

Missing Data

Outlier Detection and Robust Procedures

Mixed Continuous and Discrete Variables

Structural Risk Minimisation and the Vapnik–Chervonenkis Dimension

Bounds on the Expected Risk

The VC Dimension

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