Pattern Recognition and Classification An Introduction /

• Main Author: Dougherty, Geoff. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: New York, NY : Springer New York : Imprint: Springer, 2013.
• Subjects: Computer science.; Optical pattern recognition.; Biology > Data processing.; Algorithms.; Computer Science.; Pattern Recognition.; Nonlinear Dynamics.; Signal, Image and Speech Processing.; Computer Appl. in Life Sciences.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-5323-9

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classificati...

Table of Contents:

• Preface
• Acknowledgments
• Chapter 1 Introduction
• 1.1 Overview
• 1.2 Classification
• 1.3 Organization of the Book
• Bibliography
• Exercises
• Chapter 2 Classification
• 2.1 The Classification Process
• 2.2 Features
• 2.3 Training and Learning
• 2.4 Supervised Learning and Algorithm Selection
• 2.5 Approaches to Classification
• 2.6 Examples
• 2.6.1 Classification by Shape
• 2.6.2 Classification by Size
• 2.6.3 More Examples
• 2.6.4 Classification of Letters
• Bibliography
• Exercises
• Chapter 3 Non-Metric Methods
• 3.1 Introduction
• 3.2 Decision Tree Classifier
• 3.2.1 Information, Entropy and Impurity
• 3.2.2 Information Gain
• 3.2.3 Decision Tree Issues
• 3.2.4 Strengths and Weaknesses
• 3.3 Rule-Based Classifier
• 3.4 Other Methods
• Bibliography
• Exercises
• Chapter 4 Statistical Pattern Recognition
• 4.1 Measured Data and Measurement Errors
• 4.2 Probability Theory
• 4.2.1 Simple Probability Theory
• 4.2.2 Conditional Probability and Bayes<U+0019> Rule
• 4.2.3 Nav̐e Bayes classifier
• 4.3 Continuous Random Variables
• 4.3.1 The Multivariate Gaussian
• 4.3.2 The Covariance Matrix
• 4.3.3 The Mahalanobis Distance
• Bibliography
• Exercises
• Chapter 5 Supervised Learning
• 5.1 Parametric and Non-Parametric Learning
• 5.2 Parametric Learning
• 5.2.1 Bayesian Decision Theory
• 5.2.2 Discriminant Functions and Decision Boundaries
• 5.2.3 MAP (Maximum A Posteriori) Estimator
• Bibliography
• Exercises
• Chapter 6 Non-Parametric Learning
• 6.1 Histogram Estimator and Parzen Windows
• 6.2 k-Nearest Neighbor (k-NN) Classification
• 6.3 Artificial Neural Networks (ANNs)
• 6.4 Kernel Machines
• Bibliography
• Exercises
• Chapter 7 Feature Extraction and Selection
• 7.1 Reducing Dimensionality
• 7.1.1 Pre-Processing
• 7.2 Feature Selection
• 7.2.1 Inter/Intra-Class Distance
• 7.2.2 Subset Selection
• 7.3 Feature Extraction
• 7.3.1 Principal Component Analysis (PCA)
• 7.3.2 Linear Discriminant Analysis (LDA)
• Bibliography
• Exercises
• Chapter 8 Unsupervised Learning
• 8.1 Clustering
• 8.2 k-Means Clustering
• 8.2.1 Fuzzy c-Means Clustering
• 8.3 (Agglomerative) Hierarchical Clustering
• Bibliography
• Exercises
• Chapter 9 Estimating and Comparing Classifiers
• 9.1 Comparing Classifiers and the No Free Lunch Theorem
• 9.1.2 Bias and Variance
• 9.2 Cross-Validation and Resampling Methods
• 9.2.1 The Holdout Method
• 9.2.2 k-Fold Cross-Validation
• 9.2.3 Bootstrap
• 9.3 Measuring Classifier Performance
• 9.4 Comparing Classifiers
• 9.4.1 ROC curves
• 9.4.2 McNemar<U+0019>s Test
• 9.4.3 Other Statistical Tests
• 9.4.4 The Classification Toolbox
• 9.5 Combining classifiers
• Bibliography
• Chapter 10 Projects
• 10.1 Retinal Tortuosity as an Indicator of Disease
• 10.2 Segmentation by Texture
• 10.3 Biometric Systems
• 10.3.1 Fingerprint Recognition
• 10.3.2 Face Recognition
• Bibliography
• Index.