Anbieter: SpringBooks, Berlin, Deutschland
Erstausgabe
EUR 59,39
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In den WarenkorbHardcover. Zustand: Very Good. 1. Auflage. Unread, with a mimimum of shelfwear. Immediately dispatched from Germany.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 141,20
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In den WarenkorbZustand: New. In.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 141,20
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In den WarenkorbZustand: New. In.
Verlag: Springer International Publishing, 2020
ISBN 10: 3030407934 ISBN 13: 9783030407933
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 149,79
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In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.
Verlag: Springer International Publishing, 2021
ISBN 10: 3030407969 ISBN 13: 9783030407964
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 149,79
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.
Anbieter: Books Puddle, New York, NY, USA
EUR 177,98
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In den WarenkorbZustand: New.
Anbieter: Lucky's Textbooks, Dallas, TX, USA
EUR 148,44
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In den WarenkorbZustand: New.
Anbieter: Lucky's Textbooks, Dallas, TX, USA
EUR 148,85
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In den WarenkorbZustand: New.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 218,49
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In den WarenkorbPaperback. Zustand: Brand New. 308 pages. 9.25x6.10x1.02 inches. In Stock.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbHardcover. Zustand: Brand New. 305 pages. 9.25x6.10x9.21 inches. In Stock.
Anbieter: Mispah books, Redhill, SURRE, Vereinigtes Königreich
EUR 214,64
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In den WarenkorbHardcover. Zustand: New. New. book.
Anbieter: Mispah books, Redhill, SURRE, Vereinigtes Königreich
EUR 232,52
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In den WarenkorbPaperback. Zustand: New. New. book.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 186,58
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In den WarenkorbZustand: New. Print on Demand.
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
EUR 191,74
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In den WarenkorbZustand: New. PRINT ON DEMAND.