Principal Component Neural Networks: Theory and Applications

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9780471054368: Principal Component Neural Networks: Theory and Applications
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Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.

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Principal Component Neural Networks Theory and Applications

Understanding the underlying principles of biological perceptualsystems is of vital importance not only to neuroscientists, but,increasingly, to engineers and computer scientists who wish todevelop artificial perceptual systems. In this original andgroundbreaking work, the authors systematically examine therelationship between the powerful technique of Principal ComponentAnalysis (PCA) and neural networks. Principal Component NeuralNetworks focuses on issues pertaining to both neural network models(i.e., network structures and algorithms) and theoreticalextensions of PCA. In addition, it provides basic review materialin mathematics and neurobiology. This book presents neural modelsoriginating from both the Hebbian learning rule and least squareslearning rules, such as back-propagation. Its ultimate objective isto provide a synergistic exploration of the mathematical,algorithmic, application, and architectural aspects of principalcomponent neural networks. Especially valuable to researchers andadvanced students in neural network theory and signal processing,this book offers application examples from a variety of areas,including high-resolution spectral estimation, systemidentification, image compression, and pattern recognition.

About the Author:

K. I. Diamantaras is a research scientist at Aristotle Universityin Thessaloniki, Greece. He received his PhD from PrincetonUniversity and was formerly a research scientist for SiemansCorporate Research.

S. Y. Kung is Professor of Electrical Engineering at PrincetonUniversity and received his PhD from Stanford University. He wasformerly a professor of electrical engineering at the University ofSouthern California.

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Book Description John Wiley and Sons Ltd, United States, 1996. Hardback. Condition: New. 1. Auflage. Language: English. Brand new Book. Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas. Seller Inventory # AAH9780471054368

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Book Description John Wiley and Sons Ltd, United States, 1996. Hardback. Condition: New. 1. Auflage. Language: English . Brand New Book. Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas. Seller Inventory # AAH9780471054368

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Book Description John Wiley and Sons Ltd, United States, 1996. Hardback. Condition: New. 1. Auflage. Language: English. Brand new Book. Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas. Seller Inventory # LIE9780471054368

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