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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.
Produkteigenschaften
- Artikelnummer: 9783030407964
- Medium: Buch
- ISBN: 978-3-030-40796-4
- Verlag: Springer International Publishing
- Erscheinungstermin: 04.04.2021
- Sprache(n): Englisch
- Auflage: 1. Auflage 2020
- Serie: Information Fusion and Data Science
- Produktform: Kartoniert
- Gewicht: 470 g
- Seiten: 291
- Format (B x H x T): 155 x 235 x 17 mm
- Ausgabetyp: Kein, Unbekannt
Themen
- Technische Wissenschaften
- Elektronik | Nachrichtentechnik
- Nachrichten- und Kommunikationstechnik
- Signalverarbeitung
- Mathematik | Informatik
- EDV | Informatik
- Informatik
- Künstliche Intelligenz
- Mustererkennung, Biometrik