Feature Learning and Understanding: Algorithms and Applications - Haitao Zhao,Zhihui Lai,Henry Leung,Xianyi Zhang
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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 ... Täielik kirjeldus
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Kirjeldus
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.
Lisateave
| Autor | Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang |
|---|---|
| Kirjastaja | Springer Nature Switzerland |
| Series | Information Fusion and Data Science |
| Väljalaskeaasta | 2020 |
| Kaanetüüp | Kõvakaaneline |
| EAN | 9783030407933 |