- Išparduota
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- Autorius: Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang
- Leidėjas: Springer-Verlag GmbH
- Metai: 2020
- ISBN: 9783030407940
- ISBN-10: 3030407942
- ISBN-13: 9783030407940
- Formatas: PDF
- Kalba: Anglų
Feature Learning and Understanding (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
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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Mažiausia kaina per 30 dienų: 220,79 €
Mažiausia kaina užfiksuota: Kaina nesikeitė
- Autorius: Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang
- Leidėjas: Springer-Verlag GmbH
- Metai: 2020
- ISBN: 9783030407940
- ISBN-10: 3030407942
- ISBN-13: 9783030407940
- Formatas: PDF
- Kalba: Anglų Anglų
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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