Fine-Grained, Unsupervised, Context-based Change Detection and Adaptation for Evolving Categorical Data
Concept drift detection, the identfication of changes in data distributions in streams, is critical to understanding the mechanics of data generating processes and ensuring that data models remain representative through time [2]. Many change detection methods utilize statistical techniques that tak...
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| Language: | en |
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Université d'Ottawa / University of Ottawa
2016
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| Online Access: | http://hdl.handle.net/10393/35518 http://dx.doi.org/10.20381/ruor-476 |