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

by Carl G. Looney

Overview

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.

Product Details

ISBN-13:	9780195079203
Publisher:	Oxford University Press
Publication date:	02/20/1997
Edition description:	New Edition
Pages:	480
Product dimensions:	9.52(w) x 7.76(h) x 1.06(d)

About the Author

University of Nevada

Table of Contents

PrefaceList of TablesPart I. FUNDAMENTALS OF PATTERN RECOGNITION0.. Basic Concepts of Pattern Recognition1.. Decision-Theoretic Algorithms2.. Structural Pattern RecognitionPart II. INTRODUCTORY NEURAL NETWORKS3.. Artificial Neural Network Structures4.. Supervised Training via Error Backpropagation: DerivationsPART III. ADVANCED FUNDAMENTALS OF NEURAL NETWORKS5.. Acceleration and Stabilization of Supervised Gradient Training of MLPs6.. Supervised Training via Strategic Search7.. Advances in Network Algorithms for Classification and Recognition8.. Recurrent Neural NetworksPART IV. NEURAL, FEATURE, AND DATA ENGINEERING9.. Neural Engineering and Testing of FANNs10.. Feature and Data EngineeringPART IV. TESTING AND APPLICATIONS11.. Some Comparative Studies of Feedforward Artificial Neural Networks12.. Pattern Recognition Applications