Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds
By Isvani Inocencio Frías Blanco, José del Campo-Ávila, Gonzalo Ramos-Jiménez, Rafael Morales Bueno, Agustín Alejandro Ortiz Díaz, Yailé Caballero Mota. IEEE Transactions on Knowledge and Data Engineering, 2015
425 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/5595903937bc3840c52aa0c5e201f8e68674c733
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In Artificial Intelligence
- Barbara Hammer
- Alexander Rainer Tassilo Gepperth
- Cesare Alippi
- Jie Lu
- Guangquan Zhang
- João Gama
- Anjin Liu
- Feng Gu
Citations in the corpus
- Incremental learning algorithms and applications (2016, methodology): “Tests can rely on well-known statistics such as the Hoeffding bound [48], or alternatively on suitable distances such as the Hellinger distance, which can measure the characteristics of value distributions of such characteristic features.”
- Learning under Concept Drift: A Review (2019, methodology): “Similar implementations have been adopted and applied in the Learning with Local Drift Detection (LLDD) [25], Early Drift Detection Method (EDDM) [26], Heoffding’s inequality based Drift Detection Method (HDDM) [23], Fuzzy Windowing Drift Detection Method (FW-DDM) [5], Dynamic Extreme Learning Machi”
- Learning in Nonstationary Environments: A Survey (2015, methodology): “detection thresholds are based on Hoeffding’s Bounds is suggested in [64].”