Feature Learning and Understanding: Algorithms and Applications

di Haitao Zhao (Autore)

Zhihui Lai (Autore)

Henry Leung (Autore)

Springer Nature Switzerland AG, 2021

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.

• Autore: Haitao Zhao, Zhihui Lai, Henry Leung
• Editore: Springer Nature Switzerland AG
• Collana: Information Fusion and Data Science
• Anno: 2021
• Rilegatura: Paperback / softback
• Pagine: 291 p.
• Dimensioni: 235 x 155 mm
• EAN: 9783030407964

Testo in English