Neural Networks and Pattern Recognition Tutorial Skip Navigation Links.
Collapse Chapter 1 Pattern ClassificationChapter 1 Pattern Classification
1.1 What is Pattern Recognition?
1.2 Basics
1.3 An Example
1.4 Approaches to Pattern Recognition
Expand 1.5 Pattern Recognition Systems1.5 Pattern Recognition Systems
Collapse Chapter 2 Matrix Theory and Applications with MATLABChapter 2 Matrix Theory and Applications with MATLAB
Expand 2.1 Vectors and Matrices2.1 Vectors and Matrices
Expand 2.2 Matrix Operations in MATLAB2.2 Matrix Operations in MATLAB
Collapse Chapter 3 Network Object ReferenceChapter 3 Network Object Reference
3.1 Introduction to Programming with MATLAB
Expand 3.2 Notation in Functions3.2 Notation in Functions
3.3 Network Object Reference
Expand 3.4 Network Properties3.4 Network Properties
Collapse Chapter 4 Bayesian Decision TheoryChapter 4 Bayesian Decision Theory
4.1 Introduction
Expand 4.2 Bayesian Decision Theory (continuous)4.2 Bayesian Decision Theory (continuous)
Expand 4.3 Minimum Error Rate Classification4.3 Minimum Error Rate Classification
Expand 4.4 The Gaussian (Normal) Density4.4 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)
Collapse Chapter 5 Principal Component AnalysisChapter 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
Collapse Chapter 6 Introduction to Neural NetworksChapter 6 Introduction to Neural Networks
6.1 Introduction
6.2 Histroy of Artificial Neural Networks
Expand 6.3 How Artificial Neural Networks Are Being Used6.3 How Artificial Neural Networks Are Being Used
6.4 Summary
Collapse Chapter 7 Neural NetworkChapter 7 Neural Network
7.1 Neurophysiological Motivation
7.2 Mathematical Model of Neural Network
Expand 7.3 Neural Network7.3 Neural Network
Collapse Chapter 8 Classical Models of Neural NetworkChapter 8 Classical Models of Neural Network
8.1 The Network of Perceptrons
8.2 A Perceptron as a Pattern Classifier
Expand 8.3 Vectors8.3 Vectors
Expand 8.4 Selection of Weights for The Perceptron8.4 Selection of Weights for The Perceptron
8.5 Example
Collapse Chapter 9 Linear Discriminant FunctionsChapter 9 Linear Discriminant Functions
9.1 Introduction
Expand 9.2 Linear Discriminant Functions and Decision Surfaces9.2 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
Expand 9.6 Minimum Squared-Error Procedures9.6 Minimum Squared-Error Procedures
9.7 MATLAB Implementation
Collapse Chapter 10 Multilayer Neural NetworksChapter 10 Multilayer Neural Networks
10.1 Feedforward Operation and Classification
Expand 10.2 Backpropagation Algorithm10.2 Backpropagation Algorithm
10.3 Error Surfaces
Expand 10.4 Backpropagation as Feature Mapping10.4 Backpropagation as Feature Mapping
10.5 Backpropagation, Bayes Theory and Probability
Expand 10.6 Practical Techniques for Improving Backpropagation10.6 Practical Techniques for Improving Backpropagation
Expand 10.7 Second-Order Methods10.7 Second-Order Methods
10.8 Radial Basis Function Networks (RBFs)
Expand 10.9 MATLAB Implementation10.9 MATLAB Implementation
Collapse Chapter 11 Non-Parametric TechniquesChapter 11 Non-Parametric Techniques
11.1 Introduction
11.2 Density Estimation
Expand 11.3 Parzen Windows11.3 Parzen Windows
11.4 kn – Nearest Neighbor Estimation
11.5 MATLAB Implementation