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Parallel rule-based selective sampling and on-demand learning to rank.

, , , , , и . Concurr. Comput. Pract. Exp., (2019)

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Risk-Sensitive Learning to Rank with Evolutionary Multi-Objective Feature Selection., , , , и . ACM Trans. Inf. Syst., 37 (2): 24:1-24:34 (2019)Generalized BROOF-L2R: A General Framework for Learning to Rank Based on Boosting and Random Forests., , , и . SIGIR, стр. 95-104. ACM, (2016)Individualized extreme dominance (IndED): A new preference-based method for multi-objective recommender systems., , , , и . Inf. Sci., (2021)Risk-Sensitive Deep Neural Learning to Rank., , , и . SIGIR, стр. 803-813. ACM, (2022)Improving On-Demand Learning to Rank through Parallelism., , , , и . WISE, том 7651 из Lecture Notes in Computer Science, стр. 526-537. Springer, (2012)A Thorough Evaluation of Distance-Based Meta-Features for Automated Text Classification., , , и . IEEE Trans. Knowl. Data Eng., 30 (12): 2242-2256 (2018)Parallel rule-based selective sampling and on-demand learning to rank., , , , , и . Concurr. Comput. Pract. Exp., (2019)Incorporating Risk-Sensitiveness into Feature Selection for Learning to Rank., , , , и . CIKM, стр. 257-266. ACM, (2016)BLAST Distributed Execution on Partitioned Databases with Primary Fragments., , и . VECPAR, том 5336 из Lecture Notes in Computer Science, стр. 544-554. Springer, (2008)