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Besides other challenges in the field of streaming data classification, concept drift has to be addressed to obtain reliable predictions. Robust Soft Learning Vector Quantization as well as Generalized Learning Vector Quantization has already shown good performance in traditional settings and is modified in this work to handle streaming data. Further, momentum-based stochastic gradient descent techniques are applied to tackle concept drift passively due to increased learning capabilities. The proposed work is tested against common benchmark algorithms and streaming data in the field and achieved promising results.<\/jats:p>","DOI":"10.1007\/s00521-020-05242-6","type":"journal-article","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T16:10:19Z","timestamp":1597335019000},"page":"89-100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Passive concept drift handling via variations of learning vector quantization"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8743-1692","authenticated-orcid":false,"given":"Moritz","family":"Heusinger","sequence":"first","affiliation":[]},{"given":"Christoph","family":"Raab","sequence":"additional","affiliation":[]},{"given":"Frank-Michael","family":"Schleif","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,13]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Augenstein C, Spangenberg N, Franczyk B (2017) Applying machine learning to big data streams: an overview of challenges. 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