Pattern Recognition Using Neural Networks
Theory and Algorithms for Engineers and Scientists
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Product details:
- Publisher OUP USA
- Date of Publication 3 April 1997
- ISBN 9780195079203
- Binding Hardback
- No. of pages480 pages
- Size 243x195x26 mm
- Weight 1027 g
- Language English
- Illustrations numerous line figures, tables 0
Categories
Short description:
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. Looney has written a graduate level textbook combining the fields of pattern recognition and neural networks. It contains some theory of why the most useful networks work, the pitfalls, the algorithms to implement them, and their
applications. This text is suitable for an advanced undergraduate or graduate level course in pattern recognition or neural networks for students in computer science or electrical and computer engineering. It is also useful as a reference and a resource for practitioners and researchers.
Long description:
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.
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
Table of Contents:
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 Backpropogation: Derivations
Acceleration and Stabilization of Supervised Gradient Training of MLPs
ADVANCED FUNDAMENTALS OF NEURAL NETWORKS
Supervised Training via Strategic Search
Advances in Network Algorithms for Recognition
Using Hopfield Recurrent Neural Networks
NEURAL, FEATURE, AND DATA ENGINEERING
Neural Engineering and Testing of FANNs
Feature and Data Engineering
