Automatic machine learning: methods, systems, challenges

Frank Hutter (Redacteur), Lars Kotthoff (Redacteur), J. Vanschoren (Redacteur)

Onderzoeksoutput: Boek/rapportBoekredactieAcademicpeer review

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This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The 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. 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. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.
Originele taal-2Engels
Plaats van productieNew York
Aantal pagina's242
ISBN van elektronische versie978-3-030-05318-5
ISBN van geprinte versie978-3-030-05317-8
StatusGepubliceerd - 2019

Publicatie series

NaamChallenges in Machine Learning
ISSN van geprinte versie2520-131X

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