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Artículo: AMZ-3030407969

Feature Learning and Understanding: Algorithms and Applications (Information Fusion and Data Science)

Format:

Paperback

Hardcover

Kindle

Paperback

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En stock

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0.51 kg

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No

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Nuevo

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Amazon

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USA

Sobre este producto

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.

$317,89

44% OFF

$176,61

$317,89

44% OFF

$176,61

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