Neural Networks and Pattern Recognition Tutorial
Chapter 1 Pattern Classification
1.1 What is Pattern Recognition?
1.2 Basics
1.3 An Example
1.4 Approaches to Pattern Recognition
1.5 Pattern Recognition Systems
Chapter 2 Matrix Theory and Applications with MATLAB
2.1 Vectors and Matrices
2.2 Matrix Operations in MATLAB
Chapter 3 Network Object Reference
3.1 Introduction to Programming with MATLAB
3.2 Notation in Functions
3.3 Network Object Reference
3.4 Network Properties
Chapter 4 Bayesian Decision Theory
4.1 Introduction
4.2 Bayesian Decision Theory (continuous)
4.3 Minimum Error Rate Classification
4.4 The Gaussian (Normal) Density
4.5 Discriminant Functions, and Decision Surfaces
4.6 Discriminant Functions For The Normal Density
4.7 Bayesian Decision Theory (discrete)
References
Chapter 5 Principal Component Analysis
5.1 Introduction
5.2 Principal Component Analysis (PCA)
5.3 Principal Component Analysis in MATLAB (prepca, trapca)
5.4 Sample PCA Application in MATLAB
References
Chapter 6 Introduction to Neural Networks
6.1 Introduction
6.2 Histroy of Artificial Neural Networks
6.3 How Artificial Neural Networks Are Being Used
6.4 Summary
References
Chapter 7 Neural Network
7.1 Neurophysiological Motivation
7.2 Mathematical Model of Neural Network
7.3 Neural Network
References
Chapter 8 Classical Models of Neural Network
8.1 The Network of Perceptrons
8.2 A Perceptron as a Pattern Classifier
8.3 Vectors
8.4 Selection of Weights for The Perceptron
8.5 Example
References
Chapter 9 Linear Discriminant Functions
9.1 Introduction
9.2 Linear Discriminant Functions and Decision Surfaces
9.3 Generalized Linear Discriminant Functions
9.4 The Two-Category Linearly Separable Case
9.5 The Perceptron Criterion Function
9.6 Minimum Squared-Error Procedures
9.7 MATLAB Implementation
References
Chapter 10 Multilayer Neural Networks
10.1 Feedforward Operation and Classification
10.2 Backpropagation Algorithm
10.3 Error Surfaces
10.4 Backpropagation as Feature Mapping
10.5 Backpropagation, Bayes Theory and Probability
10.6 Practical Techniques for Improving Backpropagation
10.7 Second-Order Methods
10.8 Radial Basis Function Networks (RBFs)
10.9 MATLAB Implementation
References
Chapter 11 Non-Parametric Techniques
11.1 Introduction
11.2 Density Estimation
11.3 Parzen Windows
11.4 kn – Nearest Neighbor Estimation
11.5 MATLAB Implementation
References
