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

 
9783030053178: Automated Machine Learning: Methods, Systems, Challenges (The Springer Series on Challenges in Machine Learning)
Tapa dura
ISBN 10: 3030053172 ISBN 13: 9783030053178
Editorial: Springer-Verlag GmbH, 2019

Sinopsis

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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De la contraportada

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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Editorial
Springer-Verlag GmbH
Año de publicación
2019
Idioma
Inglés
ISBN 10 
3030053172
ISBN 13 
9783030053178
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
236
Editor
Hutter Frank, Vanschoren Joaquin, Kotthoff Lars
Contacto del fabricante
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

Europaplatz 3,69115 Heidelberg, Germany
Heidelberg
69115
Alemania

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9783030053192: Automated Machine Learning: Methods, Systems, Challenges

Edición Destacada

ISBN 10:  3030053199 ISBN 13:  9783030053192
Editorial: Springer, 2019
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