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Repondération Préférentielle pour l'Apprentissage Biqualité.

, , , and . EGC, volume E-38 of RNTI, page 339-346. Editions RNTI, (2022)

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Importance Reweighting for Biquality Learning., , , , and . IJCNN, page 1-8. IEEE, (2021)Early and Revocable Time Series Classification., , , and . IJCNN, page 1-8. IEEE, (2022)Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion., , , and . IJCNN, page 1-10. IEEE, (2023)Proactive Fiber Break Detection Based on Quaternion Time Series and Automatic Variable Selection from Relational Data., , , , , and . AALTD@PKDD/ECML, volume 11986 of Lecture Notes in Computer Science, page 26-42. Springer, (2019)Repondération Préférentielle pour l'Apprentissage Biqualité., , , and . EGC, volume E-38 of RNTI, page 339-346. Editions RNTI, (2022)Sélections simultanées de variables et de représentations pour la classification de séries temporelles., , , , and . EGC, volume E-36 of RNTI, page 415-424. Éditions RNTI, (2020)Early Classification of Time Series: Cost-based multiclass Algorithms., , , , and . DSAA, page 1-10. IEEE, (2021)From Weakly Supervised Learning to Biquality Learning, a brief introduction., , , , and . CoRR, (2020)When to Classify Events in Open Times Series?, , , and . ACML, volume 189 of Proceedings of Machine Learning Research, page 1-16. PMLR, (2022)Interpretable Feature Construction for Time Series Extrinsic Regression., , , and . PAKDD (1), volume 12712 of Lecture Notes in Computer Science, page 804-816. Springer, (2021)