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Tackling the ordinal and imbalance nature of a melanoma image classification problem., , , , и . IJCNN, стр. 2156-2163. IEEE, (2016)TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback., , , , , и . ORSUM@RecSys, том 3549 из CEUR Workshop Proceedings, CEUR-WS.org, (2023)A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings., , , и . CoRR, (2020)Ordinal Regression Methods: Survey and Experimental Study., , , , и . IEEE Trans. Knowl. Data Eng., 28 (1): 127-146 (2016)Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments., , , , и . EDM, International Educational Data Mining Society, (2022)X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI., , , , , , , и . IUI Companion, стр. 70-74. ACM, (2021)Representing ordinal input variables in the context of ordinal classification., , , и . IJCNN, стр. 2174-2181. IEEE, (2016)Ordinal Evolutionary Artificial Neural Networks for Solving an Imbalanced Liver Transplantation Problem., , , , и . HAIS, том 9648 из Lecture Notes in Computer Science, стр. 451-462. Springer, (2016)Transfer and zero-shot learning for scalable weed detection and classification in UAV images., , , , и . Knowl. Based Syst., (2024)The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models., , , и . CoRR, (2024)