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Augmentation de données pour la classification de séries temporelles par réseaux de neurones profonds résiduels.

, , , , and . EGC, volume E-35 of RNTI, page 375-376. Éditions RNTI, (2019)

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SLEMC : Apprentissage semi-supervisé enrichi par de multiples clusterings., and . Fouille de données complexes, volume E-21 of RNTI, Hermann-Éditions, (2011)Interprétation d'images basée sur une approche évolutive guidée par une ontologie., , , and . EGC, volume RNTI-E-11 of Revue des Nouvelles Technologies de l'Information, page 469-474. Cépaduès-Éditions, (2008)Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series., , , , , and . IEEE BigData, page 917-922. IEEE Computer Society, (2017)Apprentissage de règles floues pour caractériser des objets d'intérêt dans une image de télédétection., , , , and . Fouille de données complexes, volume E-27 of Revue des Nouvelles Technologies de l'Information, Hermann-Éditions, (2014)Optimal Sub-Sequence Matching for the Automatic Prediction of Surgical Tasks., , , and . AIME, volume 9105 of Lecture Notes in Computer Science, page 123-132. Springer, (2015)Automated Machine Learning for Information Retrieval in Scientific Articles., , , , , , , and . CEC, page 1-7. IEEE, (2020)Strategies for Training Stain Invariant CNNS., , , , , and . ISBI, page 905-909. IEEE, (2019)Finding Foundation Models for Time Series Classification with a PreText Task., , , , and . CoRR, (2023)Constrained Clustering: Current and New Trends., , , , and . A Guided Tour of Artificial Intelligence Research (2) (II), Springer, (2020)Toward a standard ontology of surgical process models., , , , , , , , , and 10 other author(s). Int. J. Comput. Assist. Radiol. Surg., 13 (9): 1397-1408 (2018)