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In den Warenkorbhardcover. Zustand: Sehr gut. 255 Seiten; 9780471054368.2 Gewicht in Gramm: 1.
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In den WarenkorbZustand: Gut. Zustand: Gut | Seiten: 268 | Sprache: Englisch | Produktart: Bücher.
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In den WarenkorbZustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
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In den Warenkorb24 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.
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In den WarenkorbZustand: New.
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In den WarenkorbZustand: As New. Unread book in perfect condition.
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In den WarenkorbZustand: As New. Unread book in perfect condition.
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In den WarenkorbGebunden. Zustand: 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.
Verlag: John Wiley & Sons Inc, New York, 1996
ISBN 10: 0471054364 ISBN 13: 9780471054368
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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In den WarenkorbZustand: 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. . . . .
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In den WarenkorbZustand: New. pp. 272.
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In den WarenkorbZustand: New.
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In den WarenkorbZustand: New. pp. 272.
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In den WarenkorbBuch. Zustand: Neu. Neuware - 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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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 272 pages. 9.75x6.50x0.75 inches. In Stock.
Verlag: John Wiley & Sons Inc, New York, 1996
ISBN 10: 0471054364 ISBN 13: 9780471054368
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
EUR 282,78
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In den WarenkorbZustand: 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.
Verlag: John Wiley & Sons Inc, New York, 1996
ISBN 10: 0471054364 ISBN 13: 9780471054368
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Anbieter: OM Books, Sevilla, SE, Spanien
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In den WarenkorbZustand: Usado - bueno.
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In den WarenkorbHRD. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 272 pages. 9.75x6.50x0.75 inches. In Stock. This item is printed on demand.