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The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks.
Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary.
C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method.
The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.

This book is a practical guide to the methods of application of artificial neural networks to pattern recognition in C++. It provides an intuitive explanation of each method for each network paradigm, supported by a rigorous mathematical approach where necessary.

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Introduction
Pattern Recognition Systems
Motivation for Artificial Neural Network Approach
A Prelude to Pattern Recognition
Statistical Pattern Recognition
Syntactic Pattern Recognition
The Character Recognition Problem
Organization of Topics
Neural Networks: An Overview
Motivation for Overviewing Biological Neural Networks
Background
Biological Neural Networks
Hierarchical Organization of the Brain
Historical Background
Artificial Neural Networks
Preprocessing
General
Dealing with Input from a Scanned Image
Image Compression
Edge Detection
Skeletonizing
Dealing with Input from a Tablet
Segmentation
Feed Forward Networks with Supervised Learning
Feed-Forward Multilayer Perceptron (FFMLP) Architecture
FFMLP in C++
Training with Back Propagation
A Primitive Example 
Training Strategies and Avoiding Local Minima
Variations on Gradient Descent
Topology
ACON vs. OCON
Overtraining and Generalization
Training Set Size and Network Size
Conjugate Gradient Method
ALOPEX
Some Other Types of Neural Networks
General
Radial Basis Function Networks
Higher Order Neural Networks
Feature Extraction I: Geometric Features and Transformations
General
Geometric Features (Loops, Intersections and Endpoints) 
Feature Maps
A Network Example Using Geometric Features 
Feature Extraction Using Transformations
Fourier Descriptors
Gabor Transformations and Wavelets
Feature Extraction II: Principle Component Analysis
Dimensionality Reduction
Principal Components
Karhunen-Loeve (K-L) Transformation
Principal Component Neural Networks
Applications
Kohonen Networks and Learning Vector Quantization
General
K-Means Algorithm
An Introduction to the  Kohonen Model
The Role of Lateral Feedback
Kohonen Self-Organizing Feature Map
Learning Vector Quantization
Variations on LVQ 
Neural Associative Memories and Hopfield Networks
General
Linear Associative Memory (LAM)
Hopfield Networks
A Hopfield Example
Discussion
Bit Map Example
BAM Networks
A BAM Example
Adaptive Resonance Theory (ART)
General
Discovering the Cluster Structure
Vector Quantization
ART Philosophy
The Stability-Plasticity Dilemma
Art1: Basic Operation
Art1: Algorithm
The Gain Control Mechanism
ART2 Model
Discussion
Applications
Neocognition
Introduction
Architecture
Example of a System with Sample Training Patterns
Systems with Multiple Classifiers
General
A Framework for Combining Multiple Recognizers
Voting Schemes
The Confusion Matrix
Reliability
Some Empirical Approaches

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