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oapen-20.500.12657-230122024-03-22T19:23:36Z Automated Machine Learning Hutter, Frank Kotthoff, Lars Vanschoren, Joaquin Computer science Artificial intelligence Optical data processing Pattern recognition thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQP Pattern recognition thema EDItEUR::U Computing and Information Technology::UY Computer science::UYT Image processing 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. 2020-03-18 13:36:15 2020-04-01T09:00:04Z 2020-04-01T09:00:04Z 2019 book 1007149 http://library.oapen.org/handle/20.500.12657/23012 eng The Springer Series on Challenges in Machine Learning application/pdf n/a 1007149.pdf https://www.springer.com/9783030053185 Springer Nature 10.1007/978-3-030-05318-5 10.1007/978-3-030-05318-5 6c6992af-b843-4f46-859c-f6e9998e40d5 219 Cham open access
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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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