| Description |
xix, 458 pages : illustrations ; 25 cm |
| Contents |
Ch. 0. Basic Concepts of Pattern Recognition -- Ch. 1. Decision-Theoretic Algorithms -- Ch. 2. Structural Pattern Recognition -- Ch. 3. Artificial Neural Network Structures -- Ch. 4. Supervised Training via Error Backpropagation: Derivations -- Ch. 5. Acceleration and Stabilization of Supervised Gradient Training of MLPs -- Ch. 6. Supervised Training via Strategic Search -- Ch. 7. Advances in Network Algorithms for Classification and Recognition -- Ch. 8. Recurrent Neural Networks -- Ch. 9. Neural Engineering and Testing of FANNs -- Ch. 10. Feature and Data Engineering -- Ch. 11. Some Comparative Studies of Feedforward Artificial Neural Networks -- Ch. 12. Pattern Recognition Applications |
| 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." "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."--BOOK JACKET |
| Analysis |
data engineering classification MLPs supervised gradient training error backpropagation artificial neural network structures |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Neural networks (Computer science)
|
|
Pattern recognition systems.
|
| LC no. |
96029042 |
| ISBN |
0195079205 (cloth) |
|
