Online Drift Detection with Maximum Concept Discrepancy
Author:
Affiliation:
1. University of Illinois at Urbana-Champaign, Urbana, IL, USA
2. Fudan University, Shanghai, China
3. Korea University, Seoul, Republic of Korea
Funder
Institute of Information & Communications Technology Planning Evaluation(IITP)
Korea University
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3672016
Reference55 articles.
1. Concept Drift Detection in Data Stream Mining : A literature review
2. From concept drift to model degradation: An overview on performance-aware drift detectors
3. A. Berlinet and C. Thomas-Agnan. Reproducing kernel Hilbert spaces in probability and statistics. Springer Science & Business Media, 2011.
4. Learning from Time-Changing Data with Adaptive Windowing
5. A. Bifet, R. Gavalda, G. Holmes, and B. Pfahringer. Machine learning for data streams: with practical examples in MOA. MIT press, 2023.
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