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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3156
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dc.contributor.authorTuggener, Lukas-
dc.contributor.authorAmirian, Mohammadreza-
dc.contributor.authorRombach, Katharina-
dc.contributor.authorLörwald, Stefan-
dc.contributor.authorVarlet, Anastasia-
dc.contributor.authorWestermann, Christian-
dc.contributor.authorStadelmann, Thilo-
dc.date.accessioned2019-07-17T08:55:42Z-
dc.date.available2019-07-17T08:55:42Z-
dc.date.issued2019-06-14-
dc.identifier.isbn978-1-7281-3105-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17502-
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.de_CH
dc.description.abstractA main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically – AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results of the most important AutoML algorithms.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectAutoMLde_CH
dc.subjectMeta learningde_CH
dc.subjectCASHde_CH
dc.subjectPortfolio hyperbandde_CH
dc.subjectLearning to learnde_CH
dc.subjectReinforcement learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleAutomated machine learning in practice : state of the art and recent resultsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/SDS.2019.00-11de_CH
dc.identifier.doi10.21256/zhaw-3156-
zhaw.conference.details6th Swiss Conference on Data Science (SDS), Bern, 14. Juni 2019de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end36de_CH
zhaw.pages.start31de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2019 6th Swiss Conference on Data Science (SDS)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedNatural Language Processingde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.funding.zhawAda – Advanced Algorithms for an Artificial Data Analystde_CH
zhaw.author.additionalNode_CH
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Tuggener, L., Amirian, M., Rombach, K., Lörwald, S., Varlet, A., Westermann, C., & Stadelmann, T. (2019). Automated machine learning in practice : state of the art and recent results [Conference paper]. 2019 6th Swiss Conference on Data Science (SDS), 31–36. https://doi.org/10.1109/SDS.2019.00-11
Tuggener, L. et al. (2019) ‘Automated machine learning in practice : state of the art and recent results’, in 2019 6th Swiss Conference on Data Science (SDS). IEEE, pp. 31–36. Available at: https://doi.org/10.1109/SDS.2019.00-11.
L. Tuggener et al., “Automated machine learning in practice : state of the art and recent results,” in 2019 6th Swiss Conference on Data Science (SDS), Jun. 2019, pp. 31–36. doi: 10.1109/SDS.2019.00-11.
TUGGENER, Lukas, Mohammadreza AMIRIAN, Katharina ROMBACH, Stefan LÖRWALD, Anastasia VARLET, Christian WESTERMANN und Thilo STADELMANN, 2019. Automated machine learning in practice : state of the art and recent results. In: 2019 6th Swiss Conference on Data Science (SDS). Conference paper. IEEE. 14 Juni 2019. S. 31–36. ISBN 978-1-7281-3105-4
Tuggener, Lukas, Mohammadreza Amirian, Katharina Rombach, Stefan Lörwald, Anastasia Varlet, Christian Westermann, and Thilo Stadelmann. 2019. “Automated Machine Learning in Practice : State of the Art and Recent Results.” Conference paper. In 2019 6th Swiss Conference on Data Science (SDS), 31–36. IEEE. https://doi.org/10.1109/SDS.2019.00-11.
Tuggener, Lukas, et al. “Automated Machine Learning in Practice : State of the Art and Recent Results.” 2019 6th Swiss Conference on Data Science (SDS), IEEE, 2019, pp. 31–36, https://doi.org/10.1109/SDS.2019.00-11.


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