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Author | Title | Accn# | Year | Item Type | Claims |
| 1 |
Zhao, Haitao |
Feature Learning and Understanding |
I09105 |
2020 |
eBook |
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| Title | Feature Learning and Understanding : Algorithms and Applications |
| Author(s) | Zhao, Haitao;Lai, Zhihui;Leung, Henry;Zhang, Xianyi |
| Publication | Cham, Springer International Publishing, 2020. |
| Description | XIV, 291 p. 126 illus., 109 illus. in color : online resource |
| Abstract Note | 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 |
| ISBN,Price | 9783030407940 |
| Keyword(s) | 1. Computational Intelligence
2. Data-driven Science, Modeling and Theory Building
3. EBOOK
4. EBOOK - SPRINGER
5. ECONOPHYSICS
6. IMAGE PROCESSING
7. Image Processing and Computer Vision
8. MACHINE LEARNING
9. OPTICAL DATA PROCESSING
10. PATTERN RECOGNITION
11. SIGNAL PROCESSING
12. Signal, Image and Speech Processing
13. Sociophysics
14. Speech processing systems
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| Item Type | eBook |
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