Information Fusion and Data Science- Feature Learning and Understanding Algorithms and Applications

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  • Engels
  • Hardcover
  • 9783030407933
  • 04 april 2020
  • 291 pagina's
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Samenvatting

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 featurelearning and machine intelligence.

Productspecificaties

Inhoud

Taal
en
Bindwijze
Hardcover
Oorspronkelijke releasedatum
04 april 2020
Aantal pagina's
291
Illustraties
Nee

Betrokkenen

Hoofdauteur
Haitao Zhao
Tweede Auteur
Zhihui Lai
Co Auteur
Henry Leung
Hoofduitgeverij
Springer

Overige kenmerken

Editie
1
Extra groot lettertype
Nee
Product breedte
155 mm
Product lengte
235 mm
Studieboek
Ja
Verpakking breedte
156 mm
Verpakking hoogte
19 mm
Verpakking lengte
230 mm
Verpakkingsgewicht
295 g

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EAN
9783030407933
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