Ming Li
Ming Li
Verified email at lamda.nju.edu.cn
Cited by
Cited by
Tri-training: Exploiting unlabeled data using three classifiers
ZH Zhou, M Li
IEEE Transactions on knowledge and Data Engineering 17 (11), 1529-1541, 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
Semi-supervised learning by disagreement
ZH Zhou, M Li
Knowledge and Information Systems 24 (3), 415-439, 2010
Semi-supervised regression with co-training.
ZH Zhou, M Li
IJCAI 5, 908-913, 2005
Semisupervised regression with cotraining-style algorithms
ZH Zhou, M Li
IEEE Transactions on Knowledge and Data Engineering 19 (11), 1479-1493, 2007
Sample-based software defect prediction with active and semi-supervised learning
M Li, H Zhang, R Wu, ZH Zhou
Automated Software Engineering 19 (2), 201-230, 2012
SETRED: Self-training with editing
M Li, ZH Zhou
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 611-621, 2005
Multi-instance learning based web mining
ZH Zhou, K Jiang, M Li
Applied intelligence 22 (2), 135-147, 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
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
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
Software Defect Detection with Rocus
Y Jiang, M Li, ZH Zhou
Journal of Computer Science and Technology 26 (2), 328-342, 2011
Semi-supervised document retrieval
M Li, H Li, ZH Zhou
Information Processing & Management 45 (3), 341-355, 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
Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code.
X Huo, M Li
IJCAI, 1909-1915, 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
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
Cost-effective build outcome prediction using cascaded classifiers
A Ni, M Li
2017 IEEE/ACM 14th International Conference on Mining Software Repositories …, 2017
Deep transfer bug localization
X Huo, F Thung, M Li, D Lo, ST Shi
IEEE Transactions on software engineering, 2019
Exploiting multi-modal interactions: A unified framework
M Li, XB Xue, ZH Zhou
Twenty-First International Joint Conference on Artificial Intelligence, 2009
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