Pattern Recognition Using Neural Networks : Theory and Algorithms for Engineers and Scientists
Pattern Regcognition with Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks from an algorithmic approach. The author has written a real-world practical "why-and-how" text that provides a refreshing contrast to competing texts' thoeretical appraoch and "pie-in-the-sky" claims. The text explores mulitple layered preceptrons and describes network types such as functional link, radial basis function, learning vector quantanization and self-organizing. The author also discusses recent clustering methods. This text is suitable for an advanced undergraduate course in pattern recognition or neural networks, and is also useful as a reference and a resource.
- Hardback | 480 pages
- 188 x 231.1 x 27.9mm | 1,020.59g
- 03 Apr 1997
- Oxford University Press Inc
- New York, United States
- numerous line figures, tables
Back cover copy
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. 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.
Table of contents
Part I FUNDAMENTALS OF PATTERN RECOGNITION ; 0. Basic Concepts of Pattern Recognition ; 1. Decision Theoretic Algorithms ; 2. Structural Pattern Recognition ; Part II INTRODUCTORY NEURAL NETWORKS ; 3. Artificial Neural Network Structures ; 4. Supervised Training via Error Backpropogation: Derivations ; 5. Acceleration and Stabilization of Supervised Gradient Training of MLPs ; Part III ADVANCED FUNDAMENTALS OF NEURAL NETWORKS ; 6. Supervised Training via Strategic Search ; 7. Advances in Network Algorithms for Recognition ; 8. Using Hopfield Recurrent Neural Networks ; Part IV NEURAL, FEATURE, AND DATA ENGINEERING ; 9. Neural Engineering and Testing of FANNs ; 10. Feature and Data Engineering
This is a fairly comprehensive introduction to feedforward neutral networks...the book is accessible and would be well-suited to serve as a text for its intended audience Short Book Review Vol. 17 No. 3 '... makes its subject easy to understand by offering intuitive explanations and examples... lives up to its claim as a practical neural network text and will be an excellent resource for those who want to implement neural networks, rather than just learn the theory.' Scientific Computing World, September 1997