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 Item Type eBook Multi-Media Links Please Click here for eBook Circulation Data Accession#   Call# Status Issued To Return Due On Physical Location I09105     On Shelf