book

Introduction to Pattern Recognition: A Matlab Approach

by Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, Dionisis Cavouras

March 2010

Intermediate to advanced

150 pages

7h 13m

English

Academic Press

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Front Cover
Title Page
Copyright Page
Table of Contents
Preface
Chapter 1. Classifiers Based on Bayes Decision Theory
1.1 Introduction1.2 Bayes Decision Theory
1.3 The Gaussian Probability Density Function
1.4 Minimum Distance Classifiers
1.4.1 The Euclidean Distance Classifier1.4.2 The Mahalanobis Distance Classifier
1.4.3 Maximum Likelihood Parameter Estimation of Gaussian pdfs
1.5 Mixture Models

1.6 The Expectation-Maximization Algorithm (1/2)
1.6 The Expectation-Maximization Algorithm (2/2)
1.7 Parzen Windows
1.8 k-Nearest Neighbor Density Estimation
1.9 The Naive Bayes Classifier
1.10 The Nearest Neighbor Rule
Chapter 2. Classifiers Based on Cost Function Optimization
2.1 Introduction
2.2 The Perceptron Algorithm
2.2.1 The Online Form of the Perceptron Algorithm
2.3 The Sum of Error Squares Classifier
2.3.1 The Multiclass LS Classifier
2.4 Support Vector Machines: The Linear Case
2.4.1 Multiclass Generalizations
2.5 SVM: The Nonlinear Case (1/2)
2.5 SVM: The Nonlinear Case (2/2)
2.6 The Kernel Perceptron Algorithm
2.7 The AdaBoost Algorithm
2.8 Multilayer Perceptrons (1/3)
2.8 Multilayer Perceptrons (2/3)
2.8 Multilayer Perceptrons (3/3)
Chapter 3. Data Transformation: Feature Generation and Dimensionality Reduction
3.1 Introduction3.2 Principal Component Analysis
3.3 The Singular Value Decomposition Method
3.4 Fisher’s Linear Discriminant Analysis
3.5 The Kernel PCA (1/2)
3.5 The Kernel PCA (2/2)
3.6 Laplacian Eigenmap (1/2)
3.6 Laplacian Eigenmap (2/2)
Chapter 4. Feature Selection
4.1 Introduction4.2 Outlier Removal
4.3 Data Normalization
4.4 Hypothesis Testing: The t-Test
4.5 The Receiver Operating Characteristic Curve
4.6 Fisher’s Discriminant Ratio
4.7 Class Separability Measures
4.7.1 Divergence
4.7.2 Bhattacharyya Distance and Chernoff Bound
4.7.3 Measures Based on Scatter Matrices
4.8 Feature Subset Selection
4.8.1 Scalar Feature Selection
4.8.2 Feature Vector Selection (1/3)
4.8.2 Feature Vector Selection (2/3)
4.8.2 Feature Vector Selection (3/3)
Chapter 5. Template Matching
5.1 Introduction5.2 The Edit Distance
5.3 Matching Sequences of Real Numbers
5.4 Dynamic Time Warping in Speech Recognition
Chapter 6. Hidden Markov Models
6.1 Introduction6.2 Modeling
6.3 Recognition and Training (1/3)
6.3 Recognition and Training (2/3)
6.3 Recognition and Training (3/3)
Chapter 7. Clustering
7.1 Introduction7.2 Basic Concepts and Definitions
7.3 Clustering Algorithms
7.4 Sequential Algorithms
7.4.1 BSAS Algorithm
7.4.2 Clustering Refinement (1/2)
7.4.2 Clustering Refinement (2/2)
7.5 Cost Function Optimization Clustering Algorithms
7.5.1 Hard Clustering Algorithms
7.5.2 Nonhard Clustering Algorithms
7.6 Miscellaneous Clustering Algorithms (1/2)
7.6 Miscellaneous Clustering Algorithms (2/2)
7.7 Hierarchical Clustering Algorithms
7.7.1 Generalized Agglomerative Scheme
7.7.2 Specific Agglomerative Clustering Algorithms
7.7.3 Choosing the Best Clustering (1/2)
7.7.3 Choosing the Best Clustering (2/2)
Appendix (1/2)
Appendix (2/2)
References
Index

Overview

Matlab booklet to accompany Theodoridis, Pattern Recognition 4e. Contains tutorials, examples, and Matlab code corresponding to chapters from the Pattern Recognition text.

*Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition 4e.
*Solved examples in Matlab, including real-life data sets in imaging and audio recognition
*Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)

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