Isbn: 9780471054368 - principal component neural networks: theory and applications (21 risultati)

Condizione: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

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Hardcover. Condizione: Fine. 1st Edition. No Markings.

24 cm. original hardcover. xii,256 pp. diagrams. bibliography. index. "Adaptive and Learning Systems for Signal Processing, Communications, and Control". -(owner's name, otherwise (very) good). 555g.

Condizione: Gut. Zustand: Gut | Seiten: 272 | Sprache: Englisch | Produktart: Bücher | Principal Component Neural Networks Theory and Applications Understanding the underlying principles of biological perceptual systems is of vital importance not only to neuroscientists, but, increasingly, to engineers and computer scientists who wish to develop artificial perceptual systems. In this original and groundbreaking work, the authors systematically examine the relationship between the powerful technique of Principal Component Analysis (PCA) and neural networks. Principal Component Neural Networks focuses on issues pertaining to both neural network models (i.e., network structures and algorithms) and theoretical extensions of PCA. In addition, it provides basic review material in mathematics and neurobiology. This book presents neural models originating from both the Hebbian learning rule and least squares learning rules, such as back-propagation. Its ultimate objective is to provide a synergistic exploration of the mathematical, algorithmic, application, and architectural aspects of principal component neural networks. Especially valuable to researchers and advanced students in neural network theory and signal processing, this book offers application examples from a variety of areas, including high-resolution spectral estimation, system identification, image compression, and pattern recognition.…

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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

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Condizione: New. pp. 272.

Condizione: New. 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. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 272 pages, Illustrations. BIC Classification: UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 240 x 166 x 21. Weight in Grams: 536. . 1996. 1st Edition. Hardcover. . . . .…

Condizione: New. pp. 272.

Gebunden. Condizione: New. K. I. Diamantaras is a research scientist at Aristotle University in Thessaloniki, Greece. He received his PhD from Princeton University and was formerly a research scientist for Siemans Corporate Research.S. Y. Kung is Professor of Electrical Engineering a.

Hardcover. Condizione: Brand New. 1st edition. 272 pages. 9.75x6.50x0.75 inches. In Stock.

Condizione: New. 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. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 272 pages, Illustrations. BIC Classification: UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 240 x 166 x 21. Weight in Grams: 536. . 1996. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.…

Hardcover. Condizione: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condizione: Brand New. 1st edition. 272 pages. 9.75x6.50x0.75 inches. In Stock. This item is printed on demand.