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dc.contributor.editorBeyerer, Jürgen
dc.contributor.editorKühnert, Christian
dc.contributor.editorNiggemann, Oliver
dc.date.accessioned2020-03-18 13:36:15
dc.date.accessioned2020-04-01T09:10:28Z
dc.date.available2020-04-01T09:10:28Z
dc.date.issued2019
dc.identifier1006862
dc.identifier.urihttp://library.oapen.org/handle/20.500.12657/23293
dc.description.abstractThis Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
dc.languageEnglish
dc.relation.ispartofseriesTechnologien für die intelligente Automation
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJK Communications engineering / telecommunications
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UT Computer networking & communications
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence
dc.subject.otherEngineering
dc.subject.otherComputational intelligence
dc.subject.otherComputer organization
dc.subject.otherElectrical engineering
dc.subject.otherData mining
dc.titleMachine Learning for Cyber Physical Systems
dc.title.alternativeSelected papers from the International Conference ML4CPS 2018
dc.typebook
oapen.identifier.doi10.1007/978-3-662-58485-9
oapen.relation.isPublishedBy6c6992af-b843-4f46-859c-f6e9998e40d5
oapen.pages136
oapen.place.publicationBerlin, Heidelberg


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