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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)

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From Weakly Supervised Learning to Biquality Learning, a brief introduction., , , , and . CoRR, (2020)Contrastive Representations for Label Noise Require Fine-Tuning., , , and . IAL@PKDD/ECML, volume 3079 of CEUR Workshop Proceedings, page 89-104. CEUR-WS.org, (2021)Interpretable Feature Construction for Time Series Extrinsic Regression., , , and . PAKDD (1), volume 12712 of Lecture Notes in Computer Science, page 804-816. Springer, (2021)When to Classify Events in Open Times Series?, , , and . ACML, volume 189 of Proceedings of Machine Learning Research, page 1-16. PMLR, (2022)Open challenges for Machine Learning based Early Decision-Making research., , , , , , , , and . SIGKDD Explor., 24 (2): 12-31 (2022)From Weakly Supervised Learning to Biquality Learning: an Introduction., , , , and . IJCNN, page 1-10. IEEE, (2021)FEARS: a Feature and Representation Selection approach for Time Series Classification., , , , and . ACML, volume 101 of Proceedings of Machine Learning Research, page 379-394. PMLR, (2019)Toward a Framework for Seasonal Time Series Forecasting Using Clustering., , , , , and . IDEAL (1), volume 11871 of Lecture Notes in Computer Science, page 328-340. Springer, (2019)ml_edm package: a Python toolkit for Machine Learning based Early Decision Making., , , , , , and . CoRR, (2024)Apprentissage actif d'émotions dans les dialogues Homme-Machine., , and . EGC, volume RNTI-E-9 of Revue des Nouvelles Technologies de l'Information, page 427-432. Cépaduès-Éditions, (2007)