Automated Machine Learning: Methods, Systems, Challenges (The Springer Series on Challenges in Machine Learning) - Hardcover

Buch 5 von 8: The Springer Series on Challenges in Machine Learning
 
9783030053178: Automated Machine Learning: Methods, Systems, Challenges (The Springer Series on Challenges in Machine Learning)
Hardcover
ISBN 10: 3030053172 ISBN 13: 9783030053178
Verlag: Springer, 2019

Inhaltsangabe

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. 

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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.

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Verlag
Springer
Erscheinungsdatum
2019
Sprache
Englisch
ISBN 10 
3030053172
ISBN 13 
9783030053178
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
233
Herausgeber
Hutter Frank, Kotthoff Lars, Vanschoren Joaquin
Kontakt zum Hersteller
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Deutschland

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