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Metrics for Learning in Topological Persistence

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dc.contributor.authorRiihimaki, Henri
dc.contributor.authorLicón Saláiz, José
dc.contributor.institutionUniversity of Aberdeen.Mathematical Scienceen
dc.date.accessioned2021-03-10T13:51:01Z
dc.date.available2021-03-10T13:51:01Z
dc.date.issued2019-09-16
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.description.statusPeer revieweden
dc.format.extent16
dc.format.extent1206551
dc.identifier189321374
dc.identifierfd8a0d10-e9d9-47bd-add7-e9ab84bf8267
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.doi10.20392/51hn-fj12
dc.identifier.urihttps://hdl.handle.net/2164/16009
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
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

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