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Pattern Recognition with Neural Networks in C++

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.
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Table of Contents

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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