Ankündigung Erscheint vsl. Mai 2020 Abbildung von Zhao / Lai / Leung | Feature Learning and Understanding | 1st ed. 2020 | 2020 | Algorithms and Applications

Zhao / Lai / Leung

Feature Learning and Understanding

Algorithms and Applications

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128,39 €

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1st ed. 2020 2020. Buch. xiv, 280 S. 17 s/w-Abbildungen, 109 Farbabbildungen, Bibliographien. Hardcover

Springer. ISBN 978-3-030-40793-3

Format (B x L): 15,5 x 23,5 cm

In englischer Sprache

Das Werk ist Teil der Reihe: Information Fusion and Data Science


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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