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dc.contributor.authorRiihimaki, Henri
dc.contributor.authorLicón Saláiz, José
dc.date.accessioned2021-03-10T13:51:01Z
dc.date.available2021-03-10T13:51:01Z
dc.date.issued2019-09-16
dc.identifier.citationRiihimaki , H & Licón Saláiz , J 2019 , ' Metrics for Learning in Topological Persistence ' , Paper presented at Applications of Topological Data Analysis , Würzburg , Germany , 16/09/19 - 16/09/19 . https://doi.org/10.20392/51hn-fj12en
dc.identifier.citationworkshopen
dc.identifier.otherPURE: 189321374
dc.identifier.otherPURE UUID: fd8a0d10-e9d9-47bd-add7-e9ab84bf8267
dc.identifier.urihttps://hdl.handle.net/2164/16009
dc.descriptionAcknowledgments We gratefully acknowledge Roel Neggers for providing the DALES simulation data. JLS acknowledges support by the DFG-funded transregional research collaborative TR32 on Patterns in Soil–Vegetation–Atmosphere Systems.en
dc.format.extent16
dc.language.isoeng
dc.subjectPersistent homologyen
dc.subjectTopological learningen
dc.subjectStable ranken
dc.subjectAtmospheric scienceen
dc.subjectQA Mathematicsen
dc.subject.lccQAen
dc.titleMetrics for Learning in Topological Persistenceen
dc.typeConference paperen
dc.contributor.institutionUniversity of Aberdeen.Mathematical Scienceen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.20392/51hn-fj12
dc.identifier.urlhttps://sites.google.com/view/atda2019/papersen
dc.identifier.urlhttps://sites.google.com/view/atda2019/papersen
dc.identifier.urlhttps://drive.google.com/file/d/1mSjniOKzDMm1a7D7amZPGCW-O3lZXOvn/viewen


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