An introduction to pattern recognition and machine learning / Paul Fieguth.
The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical developm...
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| Language: | English |
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[2022]
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Table of Contents:
- 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.