On Improving Semi-supervised Marginboost Incrementally using Strong Unlabeled Data. - Thanh-Binh Le; Sang-Woon Kim (ICPRAM (1), 2012)

Simply recycled selection and incrementally reinforced selection methods applicable for semi-supervised learning algorithms. - Thanh-Binh Le; Sang-Woon Kim (ICEIC, 2014)

On Selecting Helpful Unlabeled Data for Improving Semi-Supervised Support Vector Machines. - Thanh-Binh Le; Sang-Woon Kim (ICPRAM, 2014)

On incrementally using a small portion of strong unlabeled data for semi-supervised learning algorithms. - Thanh-Binh Le; Sang-Woon Kim (Pattern Recognit. Lett., 2014)

On Selecting Useful Unlabeled Data Using Multi-view Learning Techniques. - Thanh-Binh Le; Sang-Woon Kim (ICPRAM (1), 2015)

Comparison of Adjusted Methods for Selecting Useful Unlabeled Data for Semi-Supervised Learning Algorithms. - Thanh-Binh Le; Sang-Woon Kim (IEA/AIE, 2015)

Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines. - Thanh-Binh Le; Sang-Woon Kim (Pattern Recognit. Lett., 2015)

Choosing unlabeled examples for SemiBoost using modified cuckoo search algorithms. - Trung Hai Nguyen; Thanh-Binh Le; Sang-Woon Kim (ICNC-FSKD, 2016)

On measuring confidence levels using multiple views of feature set for useful unlabeled data selection. - Thanh-Binh Le; Sang-Woon Kim (Neurocomputing, 2016)

Multi-view based unlabeled data selection using feature transformation methods for semiboost learning. - Thanh-Binh Le; Sugwon Hong; Sang-Woon Kim (Neurocomputing, 2017)

Story Disambiguation: Tracking Evolving News Stories across News and Social Streams. - Bichen Shi; Thanh-Binh Le; Neil Hurley; Georgiana Ifrim (CoRR, 2018)

Building Filters for Vietnamese Chatbot Responses. - Ngoc-Thanh Le; Thanh-Binh Le; Si-Duy Truong; Hoai Bac Le (RIVF, 2020)