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Synopsis
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.
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À propos de l?auteur
Haitao Zhao is currently a full professor at the School of Information Science and Engineering, East China University of Science and Technology (ECUST), Shanghai, China. His research interests include feature extraction, representation learning, feature fusion, classifier design and their applications in image processing and computer vision.
Henry Leung is a professor of the Department of Electrical and Computer Engineering of the University of Calgary. His current research interests include information fusion, machine learning, IoT, nonlinear dynamics, robotics, signal and image processing. He is a Fellow of IEEE and SPIE.
Zhihui Lai was a Postdoctoral Fellow at the Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology (HIT) in 2011-2013. He is now a full professor at the College of Computer Science and Software Engineering, Shenzhen University.
Xianyi Zhang is a postgraduate at the School of Information Science and Engineering, East China University of Science and Technology (ECUST), Shanghai, China. His research interests include pattern recognition, machine learning and image processing.
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Feature Learning and Understanding
Edité par
Springer International Publishing, 2021
ISBN 10 : 3030407969
ISBN 13 : 9783030407964
Neuf
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers advanced feature learning methods, such as sparse learning, and deep-learning-based feature learning Includes also traditional and cutting-edge feature learning methodsContains the detailed theoretical analysis of each featu. N° de réf. du vendeur 458544751
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Feature Learning and Understanding: Algorithms and Applications (Information Fusion and Data Science)
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Feature Learning and Understanding : Algorithms and Applications
Edité par
Springer International Publishing, 2021
ISBN 10 : 3030407969
ISBN 13 : 9783030407964
Neuf
Taschenbuch
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783030407964
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Feature Learning and Understanding
Edité par
Springer International Publishing Apr 2021, 2021
ISBN 10 : 3030407969
ISBN 13 : 9783030407964
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 308 pp. Englisch. N° de réf. du vendeur 9783030407964
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Feature Learning and Understanding
ISBN 10 : 3030407969
ISBN 13 : 9783030407964
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch. N° de réf. du vendeur 9783030407964
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Feature Learning and Understanding: Algorithms and Applications (Information Fusion and Data Science)
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Feature Learning and Understanding: Algorithms and Applications (Information Fusion and Data Science)
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Feature Learning and Understanding: Algorithms and Applications (Information Fusion and Data Science)
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Feature Learning and Understanding: Algorithms and Applications (Information Fusion and Data Science)
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Feature Learning and Understanding: Algorithms and Applications
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Paperback. Etat : Brand New. 308 pages. 9.25x6.10x1.02 inches. In Stock. N° de réf. du vendeur x-3030407969
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