An online mixture-of-experts model trained with a multi-hot correctness mask matches or approaches state-of-the-art adaptive ensembles on several concept drift benchmarks.
Journal of Machine Learning Research - Proceedings Track11, 44–50 (2010)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
stat.ML 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
An online mixture-of-experts model trained with a multi-hot correctness mask matches or approaches state-of-the-art adaptive ensembles on several concept drift benchmarks.