Patching - A Framework for Adapting Immutable Classifiers to Evolving Domains
Machine learning models are subject to changing circumstances, and will degrade over time. Nowadays, data are collected in vast amounts: Personal data is retrieved by our phones, by our internet browser, via our shopping behavior, and especially through all the content that we upload to social m...
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| Format: | Others |
| Language: | en |
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2019
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| Online Access: | https://tuprints.ulb.tu-darmstadt.de/9089/1/Dissertation_SKauschke_V1-1.pdf Kauschke, Sebastian <http://tuprints.ulb.tu-darmstadt.de/view/person/Kauschke=3ASebastian=3A=3A.html> : Patching - A Framework for Adapting Immutable Classifiers to Evolving Domains. Technische Universität, Darmstadt [Ph.D. Thesis], (2019) |
Internet
https://tuprints.ulb.tu-darmstadt.de/9089/1/Dissertation_SKauschke_V1-1.pdfKauschke, Sebastian <http://tuprints.ulb.tu-darmstadt.de/view/person/Kauschke=3ASebastian=3A=3A.html> : Patching - A Framework for Adapting Immutable Classifiers to Evolving Domains. Technische Universität, Darmstadt [Ph.D. Thesis], (2019)