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100%: Haitao Zhao/ Zhihui Lai/ Henry Leung/ Xianyi Zhang: Feature Learning and Understanding (ISBN: 9783030407964) 2020, in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
100%: Haitao Zhao; Zhihui Lai; Henry Leung; Xianyi Zhang: Feature Learning and Understanding (ISBN: 9783030407940) Springer Shop, in Englisch, auch als eBook.Nur diese Ausgabe anzeigen…
Feature Learning and Understanding - 3 Angebote vergleichen
Bester Preis: Fr. 4.44 (€ 4.54)¹ (vom 28.05.2021)1
Feature Learning and Understanding
~EN PB NWISBN: 9783030407964 bzw. 3030407969, vermutlich in Englisch, Springer Shop, Taschenbuch, neu.
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Lagernd, zzgl. Versandkosten.
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. Soft cover.
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. Soft cover.
2
Feature Learning and Understanding
~EN NW EB DLISBN: 9783030407940 bzw. 3030407942, vermutlich in Englisch, Springer Shop, neu, E-Book, elektronischer Download.
Lieferung aus: Mexiko, Lagernd, zzgl. Versandkosten.
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. eBook.
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. eBook.
3
Feature Learning and Understanding (2020)
~EN PB NWISBN: 3030407969 bzw. 9783030407964, vermutlich in Englisch, Springer International Publishing, Taschenbuch, neu.
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