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Multi-view based unlabeled data selection using feature transformation methods for semiboost learning.

, , and . Neurocomputing, (2017)

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