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Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams.

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Transductive Relational Classification in the Co-training Paradigm., , , , , и . MLDM, том 7376 из Lecture Notes in Computer Science, стр. 11-25. Springer, (2012)Explorative Data Mining for the Sizing of Population Groups., , и . KDIR, стр. 152-159. INSTICC Press, (2009)Dynamic adaptation of online ensembles for drifting data streams., , и . J. Intell. Inf. Syst., 50 (2): 291-313 (2018)Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams., , и . CoRR, (2017)Physics-based measurements, reflective measurements and meta-measurements for nonrigid and deformable shapes with application to structural proteomics and macromolecular docking., , , и . CIVEMSA, стр. 112-117. IEEE, (2014)Beyond k-NN: Combining Cluster Analysis and Classification for Recommender Systems., , и . KDIR, стр. 80-89. SciTePress, (2018)New Graph Distance Based on Stable Marriage Formulation for Deformable 3D Objects Recognition., , и . AICCSA, стр. 1-8. IEEE Computer Society, (2019)A Framework for Classification in Data Streams Using Multi-strategy Learning., , и . DS, том 9956 из Lecture Notes in Computer Science, стр. 341-355. (2016)Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation., , и . NFMCP, том 7765 из Lecture Notes in Computer Science, стр. 49-63. Springer, (2012)Intelligent Adaptive Ensembles for Data Stream Mining: A High Return on Investment Approach., , и . NFMCP, том 9607 из Lecture Notes in Computer Science, стр. 61-75. Springer, (2015)