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dc.contributor.editorHutter, Frank
dc.contributor.editorKotthoff, Lars
dc.contributor.editorVanschoren, Joaquin
dc.date.accessioned2021-02-10T13:50:03Z
dc.date.available2021-02-10T13:50:03Z
dc.date.issued2019
dc.identifier1007149
dc.identifierhttp://library.oapen.org/handle/20.500.12657/23012
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/31379
dc.description.abstractThis 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.
dc.languageEnglish
dc.relation.ispartofseriesThe Springer Series on Challenges in Machine Learning
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQP Pattern recognition
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYT Image processing
dc.subject.otherComputer science
dc.subject.otherArtificial intelligence
dc.subject.otherOptical data processing
dc.subject.otherPattern recognition
dc.titleAutomated Machine Learning
dc.title.alternativeMethods, Systems, Challenges
dc.typebook
oapen.identifier.doi10.1007/978-3-030-05318-5
oapen.relation.isPublishedBy9fa3421d-f917-4153-b9ab-fc337c396b5a
oapen.pages219
oapen.place.publicationCham
dc.dateSubmitted2020-03-18 13:36:15
dc.dateSubmitted2020-04-01T09:00:04Z


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