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Classifying and Counting with Recurrent Contexts., , , and . KDD, page 1983-1992. ACM, (2018)DyS: A Framework for Mixture Models in Quantification., , , and . AAAI, page 4552-4560. AAAI Press, (2019)Time Series Classification with Motifs and Characteristics., , , , , , and . Soft Computing for Business Intelligence, volume 537 of Studies in Computational Intelligence, Springer, (2014)Accurately Quantifying a Billion Instances per Second., , and . DSAA, page 1-10. IEEE, (2020)Accurately Quantifying under Score Variability., , , and . ICDM, page 1228-1233. IEEE, (2021)On the Need of Class Ratio Insensitive Drift Tests for Data Streams., , , and . LIDTA@ECML/PKDD, volume 94 of Proceedings of Machine Learning Research, page 110-124. PMLR, (2018)Pitfalls in Quantification Assessment., , and . CIKM Workshops, volume 3052 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Quantification in Data Streams: Initial Results., , and . BRACIS, page 43-48. IEEE Computer Society, (2017)Unsupervised context switch for classification tasks on data streams with recurrent concepts., , and . SAC, page 518-524. ACM, (2018)One-Class Quantification., , , and . ECML/PKDD (1), volume 11051 of Lecture Notes in Computer Science, page 273-289. Springer, (2018)