Intro
Preface
Table of Contents
List of Examples
List of Algorithms
Notation
1 Overview
2 Introduction to Pattern Recognition
2.1 What Is Pattern Recognition?
2.2 Measured Patterns
2.3 Classes
2.4 Classification
2.5 Types of Classification Problems
Case Study 2: Biometrics
Numerical Lab 2: The Iris Dataset
Further Reading
Sample Problems
References
3 Learning
Case Study 3: The Netflix Prize
Numerical Lab 3: Overfitting and Underfitting
Summary
Further Reading
Sample Problems
References
4 Representing Patterns

4.1 Similarity
4.2 Class Shape
4.3 Cluster Synthesis
Case Study 4: Defect Detection
Numerical Lab 4: Working with Random Numbers
Further Reading
Sample Problems
References
5 Feature Extraction and Selection
5.1 Fundamentals of Feature Extraction
5.2 Feature Extraction and Selection
Case Study 5: Image Searching
Numerical Lab 5: Extracting Features and Plotting Classes
Further Reading
Sample Problems
References
6 Distance-Based Classification
6.1 Definitions of Distance
6.2 Class Prototype
6.3 Distance-Based Classification

6.4 Classifier Variations
Case Study 6: Hand-writing Recognition
Numerical Lab 6: Distance-Based Classifiers
Further Reading
Sample Problems
References
7 Inferring Class Models
7.1 Parametric Estimation
7.2 Parametric Model Learning
7.3 Nonparametric Model Learning
7.3.1 Histogram Estimation
7.3.2 Kernel-Based Estimation
7.3.3 Neighbourhood-based Estimation
7.4 Distribution Assessment
Case Study 7: Object Recognition
Numerical Lab 7: Parametric and Nonparametric Estimation
Further Reading
Sample Problems
References

8 Statistics-Based Classification
8.1 Non-Bayesian Classification: Maximum Likelihood
8.2 Bayesian Classification: Maximum a Posteriori
8.3 Statistical Classification for Normal Distributions
8.4 Classification Error
8.5 Other Statistical Classifiers
Case Study 8: Medical Assessments
Numerical Lab 8: Statistical and Distance-Based Classifiers
Further Reading
Sample Problems
References
9 Classifier Testing and Validation
9.1 Working with Data
9.2 Classifier Evaluation
9.3 Classifier Validation
Case Study 9: Autonomous Vehicles

Numerical Lab 9: Leave-One-Out Validation
Further Reading
Sample Problems
References
10 Discriminant-Based Classification
10.1 Linear Discriminants
10.2 Discriminant Model Learning
10.3 Nonlinear Discriminants
10.4 Multi-Class Problems
Case Study 10: Digital Communications
Numerical Lab 10: Discriminants
Further Reading
Sample Problems
References
11 Ensemble Classification
11.1 Combining Classifiers
11.2 Resampling Strategies
11.3 Sequential Strategies
11.4 Nonlinear Strategies
11.4.1 Neural Network Learning