Feature Learning and Understanding
Produktnummer:
187498c85a50624e6dae2c00d47a0be2a5
| Autor: | Lai, Zhihui Leung, Henry Zhang, Xianyi Zhao, Haitao |
|---|---|
| Themengebiete: | data analysis feature learning linear discriminant analysis low rank decomposition machine intelligence machine learning pattern recognition principal component analysis sparse learning tensor-based feature extraction |
| Veröffentlichungsdatum: | 04.04.2020 |
| EAN: | 9783030407933 |
| Sprache: | Englisch |
| Seitenzahl: | 291 |
| Produktart: | Gebunden |
| Verlag: | Springer International Publishing |
| Untertitel: | Algorithms and Applications |
Produktinformationen "Feature Learning and Understanding"
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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