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