Automated Machine Learning Methods, Systems, Challenges

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial...

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Bibliographische Detailangaben
Hauptverfasser: Hutter, Frank, Kotthoff, Lars, Vanschoren, Joaquin
Format: E-Book
Sprache:Englisch
Veröffentlicht: Cham Springer Nature 2019
Springer International Publishing AG
Ausgabe:1
Schriftenreihe:The Springer Series on Challenges in Machine Learning
Schlagworte:
Artificial intelligence
Computer science
Computer Technology
Computing and Information Technology
Image processing
Nonfiction
Optical data processing
Pattern recognition
ISBN:3030053172, 9783030053178, 3030053180, 9783030053185
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Bibliographie:Electronic reproduction. Dordrecht: Springer, 2019. Requires the Libby app or a modern web browser.
ISBN:3030053172
9783030053178
3030053180
9783030053185
DOI:10.1007/978-3-030-05318-5