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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 - gebrauchtes Buch

ISBN: 9780195079203

ID: 9780195079203

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 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. Textbooks New, Books~~Computers~~Neural Networks, Pattern-Recognition-Using-Neural-Networks~~Carl-G-Looney, , , , , , , , , , Oxford University Press, USA

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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - Carl G. Looney
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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - neues Buch

ISBN: 9780195079203

ID: 978019507920

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 theoreticalapproach 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 recursiveneural 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, andgenetic 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. Carl G. Looney, Books, Computers, Programming, Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists Books>Computers>Programming, Oxford University Press

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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - Carl G. Looney
Vergriffenes Buch, derzeit bei uns nicht verfügbar.
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Carl G. Looney:
Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - neues Buch

ISBN: 9780195079203

ID: 978019507920

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 theoreticalapproach 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 recursiveneural 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, andgenetic 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. Carl G. Looney, Books, Computers, Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists Books>Computers, Oxford University Press

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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - Looney, Carl G.
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Looney, Carl G.:
Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - gebrauchtes Buch

ISBN: 9780195079203

ID: 535033

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. Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists Looney, Carl G., Oxford University Press, USA

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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - Looney, Carl G.
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Looney, Carl G.:
Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists - gebunden oder broschiert

1997, ISBN: 0195079205

ID: 14096966349

[EAN: 9780195079203], [PU: Oxford University Press], Computers & the Internet|Computer Vision, Computers & the Internet|Data Processing|General, Computers & the Internet|Neural Networks, This Book is in Good Condition. Clean Copy With Light Amount of Wear. 100% Guaranteed. Summary: Preface List of Tables 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 Backpropagation: Derivations PART III. ADVANCED FUNDAMENTALS OF NEURAL NETWORKS 5. Acceleration and Stabilization of Supervised Gradient Training of MLPs 6. Supervised Training via Strategic Search 7. Advances in Network Algorithms for Classification and Recognition 8. Recurrent Neural Networks PART IV. NEURAL, FEATURE, AND DATA ENGINEERING 9. Neural Engineering and Testing of FANNs 10. Feature and Data Engineering PART IV. TESTING AND APPLICATIONS 11. Some Comparative Studies of Feedforward Artificial Neural Networks 12. Pattern Recognition Applications

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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.

Detailangaben zum Buch - Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists


EAN (ISBN-13): 9780195079203
ISBN (ISBN-10): 0195079205
Gebundene Ausgabe
Erscheinungsjahr: 1997
Herausgeber: OXFORD UNIV PR
480 Seiten
Gewicht: 1,007 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 17.05.2007 17:54:31
Buch zuletzt gefunden am 27.07.2017 07:47:48
ISBN/EAN: 0195079205

ISBN - alternative Schreibweisen:
0-19-507920-5, 978-0-19-507920-3


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