| Tri-training: Exploiting unlabeled data using three classifiers ZH Zhou, M Li IEEE Transactions on knowledge and Data Engineering 17 (11), 1529-1541, 2005 | 1362 | 2005 |
| Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples M Li, ZH Zhou IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and …, 2007 | 451 | 2007 |
| Semi-supervised learning by disagreement ZH Zhou, M Li Knowledge and Information Systems 24, 415-439, 2010 | 448 | 2010 |
| Semi-supervised regression with co-training. ZH Zhou, M Li IJCAI 5, 908-913, 2005 | 377 | 2005 |
| Semisupervised regression with cotraining-style algorithms ZH Zhou, M Li IEEE Transactions on Knowledge and Data Engineering 19 (11), 1479-1493, 2007 | 240 | 2007 |
| Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code. H Wei, M Li IJCAI, 3034-3040, 2017 | 228 | 2017 |
| Sample-based software defect prediction with active and semi-supervised learning M Li, H Zhang, R Wu, ZH Zhou Automated Software Engineering 19, 201-230, 2012 | 218 | 2012 |
| SETRED: Self-training with editing M Li, ZH Zhou Advances in Knowledge Discovery and Data Mining: 9th Pacific-Asia Conference …, 2005 | 195 | 2005 |
| Learning unified features from natural and programming languages for locating buggy source code. X Huo, M Li, ZH Zhou IJCAI 16, 1606-1612, 2016 | 175 | 2016 |
| Multi-instance learning based web mining ZH Zhou, K Jiang, M Li Applied intelligence 22, 135-147, 2005 | 163 | 2005 |
| Online manifold regularization: A new learning setting and empirical study A Goldberg, M Li, X Zhu Machine Learning and Knowledge Discovery in Databases, 393-407, 2008 | 120 | 2008 |
| Software defect detection with ROCUS Y Jiang, M Li, ZH Zhou Journal of Computer Science and Technology 26 (2), 328, 2011 | 82 | 2011 |
| Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code. X Huo, M Li IJCAI, 1909-1915, 2017 | 72 | 2017 |
| Distributed deep forest and its application to automatic detection of cash-out fraud YL Zhang, J Zhou, W Zheng, J Feng, L Li, Z Liu, M Li, Z Zhang, C Chen, ... ACM Transactions on Intelligent Systems and Technology (TIST) 10 (5), 1-19, 2019 | 67 | 2019 |
| Semi-supervised document retrieval M Li, H Li, ZH Zhou Information Processing & Management 45 (3), 341-355, 2009 | 66 | 2009 |
| Learning instance specific distances using metric propagation DC Zhan, M Li, YF Li, ZH Zhou Proceedings of the 26th annual international conference on machine learning …, 2009 | 62 | 2009 |
| Deep transfer bug localization X Huo, F Thung, M Li, D Lo, ST Shi IEEE Transactions on software engineering 47 (7), 1368-1380, 2019 | 49 | 2019 |
| Cost-effective build outcome prediction using cascaded classifiers A Ni, M Li 2017 IEEE/ACM 14th International Conference on Mining Software Repositories …, 2017 | 35 | 2017 |
| Positive and Unlabeled Learning for Detecting Software Functional Clones with Adversarial Training. H Wei, M Li IJCAI, 2840-2846, 2018 | 29 | 2018 |
| Mining extremely small data sets with application to software reuse Y Jiang, M Li, ZH Zhou Software: Practice and Experience 39 (4), 423-440, 2009 | 29 | 2009 |
Zhi-Hua ZhouNanjing UniversityAdresse e-mail validée de nju.edu.cn
