| 書(shū)目名稱 | Feature Learning and Understanding |
| 副標(biāo)題 | Algorithms and Appli |
| 編輯 | Haitao Zhao,Zhihui Lai,Xianyi Zhang |
| 視頻video | http://file.papertrans.cn/342/341562/341562.mp4 |
| 概述 | Offers advanced feature learning methods, such as sparse learning, and deep-learning-based feature learning.Includes also traditional and cutting-edge feature learning methods.Contains the detailed th |
| 叢書(shū)名稱 | Information Fusion and Data Science |
| 圖書(shū)封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2020 |
| 關(guān)鍵詞 | feature learning; machine learning; pattern recognition; data analysis; principal component analysis; lin |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-40794-0 |
| isbn_softcover | 978-3-030-40796-4 |
| isbn_ebook | 978-3-030-40794-0Series ISSN 2510-1528 Series E-ISSN 2510-1536 |
| issn_series | 2510-1528 |
| copyright | Springer Nature Switzerland AG 2020 |
|Archiver|手機(jī)版|小黑屋|
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