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dc.contributor.authorGygax, Gregory-
dc.contributor.authorFüchslin, Rudolf Marcel-
dc.contributor.authorOtt, Thomas-
dc.date.accessioned2020-11-19T10:42:59Z-
dc.date.available2020-11-19T10:42:59Z-
dc.date.issued2020-11-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20862-
dc.description.abstractWe present the idea of a self-organized division of labor in networks of forecasting models. We find that the principles of self-organizing maps provide a good starting point for building resilient machine learning systems based on our idea. The potential of the idea, benefits and challenges are discussed by means of two toy-like problems.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSelf-organizationde_CH
dc.subjectResilient machine learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleSelf-organized division of labor in networks of forecasting models for time series with regime switchesde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Angewandte Simulation (IAS)de_CH
zhaw.conference.details2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), Online Conference, 16-19 November 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end281de_CH
zhaw.pages.start278de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.title.proceedingsProceedings of the NOLTA 2020 Conferencede_CH
zhaw.webfeedBio-Inspired Modelling and Learning Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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