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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - Hardcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
Carl Grant Looney is Professor and Director of the Graduate Program in the Computer Science Department at the University of Nevada in Reno.
Review
"Pattern Recognition Using Neural Networks makes its subject easy to understand by offering intuitive explanations and examples. ...an excellent 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
"About this title" may belong to another edition of this title.
- PublisherOxford University Press
- Publication date1997
- ISBN 10 0195079205
- ISBN 13 9780195079203
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages480
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Pattern Recognition Using Neural Networks (Hardcover)
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Oxford University Press Inc, New York, 1997
ISBN 10: 0195079205
ISBN 13: 9780195079203
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Hardcover. Condition: new. Hardcover. 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, functionallink 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 fuzzyclustering 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 andresource for researchers and professionals. A text covering traditional linear pattern recognition and its nonlinear extension via neural networks. The approach is algorithmic for easy implementation on a computer. It covers the standard decision-theoretic pattern recognition of clustering via minimum distance, graphical and structural methods, and Bayesian discrimination. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780195079203
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Pattern Recognition Using Neural Networks : Theory and Algorithms for Engineers and Scientists
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ISBN 10: 0195079205
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Pattern Recognition Using Neural Networks (Hardcover)
Published by
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ISBN 10: 0195079205
ISBN 13: 9780195079203
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Hardcover. Condition: new. Hardcover. 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, functionallink 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 fuzzyclustering 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 andresource for researchers and professionals. A text covering traditional linear pattern recognition and its nonlinear extension via neural networks. The approach is algorithmic for easy implementation on a computer. It covers the standard decision-theoretic pattern recognition of clustering via minimum distance, graphical and structural methods, and Bayesian discrimination. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9780195079203
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Pattern Recognition Using Neural Networks : Theory and Algorithms for Engineers and Scientists
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Pattern Recognition Using Neural Networks : Theory and Algorithms for Engineers and Scientists
Published by
Oxford University Press, 1997
ISBN 10: 0195079205
ISBN 13: 9780195079203
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Pattern Recognition Using Neural Networks
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ISBN 10: 0195079205
ISBN 13: 9780195079203
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Buch. Condition: Neu. Neuware - 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 minimumdistance, 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. Seller Inventory # 9780195079203
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