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On measuring confidence levels using multiple views of feature set for useful unlabeled data selection.

, и . Neurocomputing, (2016)

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Vu Thanh Binh

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Multi-view based unlabeled data selection using feature transformation methods for semiboost learning., , и . Neurocomputing, (2017)On Selecting Helpful Unlabeled Data for Improving Semi-Supervised Support Vector Machines., и . ICPRAM, стр. 48-59. SciTePress, (2014)Comparison of Adjusted Methods for Selecting Useful Unlabeled Data for Semi-Supervised Learning Algorithms., и . IEA/AIE, том 9101 из Lecture Notes in Computer Science, стр. 526-535. Springer, (2015)Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines., , , , , , , , , и 2 other автор(ы). IEEE Access, (2021)Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines., и . Pattern Recognit. Lett., (2015)Simply recycled selection and incrementally reinforced selection methods applicable for semi-supervised learning algorithms., и . ICEIC, стр. 1-2. IEEE, (2014)On measuring confidence levels using multiple views of feature set for useful unlabeled data selection., и . Neurocomputing, (2016)On Improving Semi-supervised Marginboost Incrementally using Strong Unlabeled Data., и . ICPRAM (1), стр. 265-268. SciTePress, (2012)Choosing unlabeled examples for SemiBoost using modified cuckoo search algorithms., , и . ICNC-FSKD, стр. 534-541. IEEE, (2016)Building Filters for Vietnamese Chatbot Responses., , , и . RIVF, стр. 1-6. IEEE, (2020)