Pattern Recognition, 4th Edition

Table of Contents

Copyright

Preface

Chapter 1. Introduction

1.1. Is Pattern Recognition Important?

1.2. Features, Feature Vectors, and Classifiers

1.3. Supervised, Unsupervised, and Semi-Supervised Learning

1.4. MATLAB Programs

1.5. Outline of the Book

Chapter 2. Classifiers Based on Bayes Decision Theory

2.1. Introduction

2.2. Bayes Decision Theory

2.3. Discriminant Functions and Decision Surfaces

2.4. Bayesian Classification for Normal Distributions

2.4.1. The Gaussian Probability Density Function

2.4.2. The Bayesian Classifier for Normally Distributed Classes

2.5. Estimation of Unknown Probability Density Functions

2.5.1. Maximum Likelihood Parameter Estimation

2.5.2. Maximum a Posteriori Probability Estimation

2.5.3. Bayesian Inference ...