Features
- Cover Type: Hard Cover with 480 pages
- Published by: Oxford University Press, USA February 20, 1997
- Written in: English
- ISBN 10 Number: 0195079205
- ISBN 13 Number: 978-0195079203
-
Book Dimensions:
9.1 x 7.4 x 1.1 inches
- Weighs: 2.2 pounds
Product Review
"Pattern Recognition Using Neural Networks makes its subject easy to understand by offering intuitive explanations and examples. an great resource for those who want to implement neural networks, rather than just learn the theory."--Mark Kvale,
"Really good text for students and professionals."--Aiy Farag, University of Louisville
Book 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. 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.
Reader Reviews
This is simply the best introduction in print to the most useful types of neural networks that engineers use. Engineering seniors and graduate students should benefit greatly. I was quite impressed at the full-propagation algorithm that is 40% faster than using epochs with backpropagation that tend to thrash (learn/unlearn as it trains). I found the book loaded with ideas and references and have put some of them to good use so far. This was the book I really needed but never knew about until my professor told me to read the section on radial basis function NNs. I put the pseudo-code directly into a C program and it worked beautifully on my data.
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