Pattern Recognition Using Neural Networks Theory and Algorithms for Engineers and Scientists

by Looney, Carl G.

• ISBN13: 9780195079203
• ISBN10: 0195079205
• Format: Hardcover
• Copyright: 1997-02-20
• Publisher: Oxford University Press

Summary

Pattern Recognition Using Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks. The approach is algorithmic for easy implementation on a computer, which makes this a refreshing what-why-and-how text that contrasts with the theoretical approach and pie-in-the-sky hyperbole of many books on neural networks. It covers the standard decision-theoretic pattern recognition of clustering via minimum distance, graphical and structural methods, and Bayesian discrimination. Pattern recognizers evolve across the sections into perceptrons, a layer of perceptrons, multiple-layered perceptrons, functional link nets, and radial basis function networks. Other networks covered in the process are learning vector quantization networks, self-organizing maps, and recursive neural networks. Backpropagation is derived in complete detail for one and two hidden layers for both unipolar and bipolar sigmoid activation functions. The more efficient fullpropagation, quickpropagation, cascade correlation, and various methods such as strategic search, conjugate gradients, and genetic algorithms are described. Advanced methods are also described, including the full training algorithms for radial basis function networks and random vector functional link nets, as well as competitive learning networks and fuzzy clustering algorithms. Special topics covered include: feature engineering data engineering neural engineering of network architectures validation and verification of the trained networks This textbook is ideally suited for a senior undergraduate or graduate course in pattern recognition or neural networks for students in computer science, electrical engineering, and computer engineering. It is also a useful reference and resource for researchers and professionals.

Author Biography

Carl Grant Looney is Professor and Director of the Graduate Program in the Computer Science Department at the University of Nevada in Reno.

Table of Contents

Preface

List of Tables

Fundamentals of Pattern Recognition

Basic Concepts of Pattern Recognition

Decision-Theoretic Algorithms

Structural Pattern Recognition

Introductory Neural Networks

Artificial Neural Network Structures

Supervised Training via Error Backpropagation: Derivations

Advanced Fundamentals of Neural Networks

Acceleration and Stabilization of Supervised Gradient Training of MLPs

Supervised Training via Strategic Search

Advances in Network Algorithms for Classification and Recognition

Recurrent Neural Networks

Neural, Feature, and Data Engineering

Neural Engineering and Testing of FANNs

Feature and Data Engineering

Testing and Applications

Some Comparative Studies of Feedforward Artificial Neural Networks

Pattern Recognition Applications

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