Introduction to Pattern Recognition: A Matlab Approach book cover

Introduction to Pattern Recognition: A Matlab Approach

An accompanying manual to Theodoridis/Koutroumbas, Pattern Recognition, that includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.

Electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning. R&D engineers and university researchers in image and signal processing/analyisis, and computer vision;

Paperback, 231 Pages

Published: March 2010

Imprint: Academic Press

ISBN: 978-0-12-374486-9


  • Preface

    Chapter 1. Classifiers Based on Bayes Decision Theory

    1.1 Introduction

    1.2 Bayes Decision Theory

    1.3 The Gaussian Probability Density Function

    1.4 Minimum Distance Classifiers

    1.4.1 The Euclidean Distance Classifier

    1.4.2 The Mahalanobis Distance Classifier

    1.4.3 Maximum Likelihood Parameter Estimation of Gaussian pdfs

    1.5 Mixture Models

    1.6 The Expectation-Maximization Algorithm

    1.7 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

    2.6 The Kernel Perceptron Algorithm

    2.7 The AdaBoost Algorithm

    2.8 Multilayer Perceptrons

    Chapter 3. Data Transformation: Feature Generation and Dimensionality Reduction

    3.1 Introduction

    3.2 Principal Component Analysis

    3.3 The Singular Value Decomposition Method

    3.4 Fisher's Linear Discriminant Analysis

    3.5 The Kernel PCA

    3.6 Laplacian Eigenmap

    Chapter 4. Feature Selection

    4.1 Introduction

    4.2 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

    Chapter 5. Template Matching

    5.1 Introduction

    5.2 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 Introduction

    6.2 Modeling

    6.3 Recognition and Training

    Chapter 7. Clustering

    7.1 Introduction

    7.2 Basic Concepts and Definitions

    7.3 Clustering Algorithms

    7.4 Sequential Algorithms

    7.4.1 BSAS Algorithm

    7.4.2 Clustering Refinement

    7.5 Cost Function Optimization Clustering Algorithms

    7.5.1 Hard Clustering Algorithms

    7.5.2 Nonhard Clustering Algorithms

    7.6 Miscellaneous Clustering Algorithms

    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





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