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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 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.
| Autorius: | Haitao Zhao, Xianyi Zhang, Henry Leung, Zhihui Lai, |
| Serija: | Information Fusion and Data Science |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 308 |
| ISBN-10: | 3030407969 |
| ISBN-13: | 9783030407964 |
| Formatas: | 235 x 155 x 17 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Feature Learning and Understanding: Algorithms and Applications“
