CRC Press - Pattern Recognition with Neural Networks in C++ (1995) (rebuiltSep04,complete)
Main Page
Preface
Acknowledgment
1. Introduction
1.1 Pattern Recognition Systems
1.2 Motivation For Artificial Neural Network Approach
1.3 A Prelude To Pattern Recognition
1.4 Statistical Pattern Recognition
1.5 Syntactic Pattern Recognition
1.6 The Character Recognition Problem
1.7 Organization Of Topics
References And Bibliography
2. Neural Networks: An Overview
2.1 Motivation for Overviewing Biological Neural Networks
2.2 Background
2.3 Biological Neural Networks
2.4 Hierarchical Organization in the Brain
2.5 Historical Background
2.6 Artificial Neural Networks
References and Bibliography
3. Preprocessing
3.1 General
3.2 Dealing with Input from a Scanned Image
3.3 Image Compression
3.3.1 Image Compression Example
3.4 Edge Detection
3.5 Skeletonizing
3.5.1 Thinning Example
3.6 Dealing with Input From a Tablet
3.7 Segmentation
References and Bibliography
4. Feed-Forward Networks with Supervised Learning
4.1 Feed-Forward Multilayer Perceptron (FFMLP) Architecture
4.2 FFMLP in C++
4.3 Training with Back Propagation
4.3.1 Back Propagation in C++
4.4 A Primitive Example
4.5 Training Strategies and Avoiding Local Minima
4.6 Variations on Gradient Descent
4.6.1 Block Adaptive vs. Data Adaptive Gradient Descent
4.6.2 First-Order vs. Second-Order Gradient Descent
4.7 Topology
4.8 ACON vs. OCON
4.9 Overtraining and Generalization
4.10 Training Set Size and Network Size
4.11 Conjugate Gradient Method
4.12 ALOPEX
References and Bibliography
5. Some Other Types of Neural Networks
5.1 General
5.2 Radial Basis Function Networks
5.2.1 Network Architecture
5.2.2 RBF Training
5.2.3 Applications of RBF Networks
5.3 Higher Order Neural Networks
5.3.1 Introduction
5.3.2 Architecture
5.3.3 Invariance to Geometric Transformations
5.3.4 An Example
5.3.5 Practical Applications
References and Bibliography
6. Feature Extraction I: Geometric Features and Transformations
6.1 General
6.2 Geometric Features (Loops, Intersections, and Endpoints)
6.2.1 Intersections and Endpoints
6.2.2 Loops
6.3 Feature Maps
6.4 A Network Example Using Geometric Features
6.5 Feature Extraction Using Transformations
6.6 Fourier Descriptors
6.7 Gabor Transformations and Wavelets
References And Bibliography
7. Feature Extraction II: Principal Component Analysis
7.1 Dimensionality Reduction
7.2 Principal Components
7.2.1 PCA Example
7.3 Karhunen-Loeve (K-L) Transformation
7.3.1 K-L Transformation Example
7.4 Principal Component Neural Networks
7.5 Applications
References and Bibliography
8. Kohonen Networks and Learning Vector Quantization
8.1 General
8.2 The K-Means Algorithm
8.2.1 K-Means Example
8.3 An Introduction To The Kohonen Model
8.3.1 Kohonen Example
8.4 The Role Of Lateral Feedback
8.5 Kohonen Self-Organizing Feature Map
8.5.1 SOFM Example
8.6 Learning Vector Quantization
8.6.1 LVQ Example
8.7 Variations On LVQ
8.7.1 LVQ2
8.7.2 LVQ2.1
8.7.3 LVQ3
8.7.4 A Final Variation Of LVQ
References And Bibliography
9. Neural Associative Memories and Hopfield Networks
9.1 General
9.2 Linear Associative Memory (LAM)
9.2.1 An Autoassociative LAM Example
9.3 Hopfield Networks
9.4 A Hopfield Example
9.5 Discussion
9.6 Bit Map Example
9.7 Bam Networks
9.8 A Bam Example
References And Bibliography
10. Adaptive Resonance Theory (ART)
10.1 General
10.2 Discovering The Cluster Structure
10.3 Vector Quantization
10.3.1 VQ Example 1
10.3.2 VQ Example 2
10.3.3 VQ Example 3
10.4 Art Philosophy
10.5 The Stability-Plasticity Dilemma
10.6 ART1: Basic Operation
10.7 ART1: Algorithm
10.8 The Gain Control Mechanism
10.8.1 Gain Ccontrol Example 1
10.8.2 Gain Control Example 2
10.9 ART2 Model
10.10 Discussion
10.11 Applications
References and Bibliography
11. Neocognitron
11.1 Introduction
11.2 Architecture
11.3 Example of a System with Sample Training Patterns
References and Bibliography
12. Systems with Multiple Classifiers
12.1 General
12.2 A Framework for Combining Multiple Recognizers
12.3 Voting Schemes
12.4 The Confusion Matrix
12.5 Reliability
12.6 Some Empirical Approaches
References and Bibliography
Index