M
Ming Li
Researcher at Nanjing University
Publications - 53
Citations - 3808
Ming Li is an academic researcher from Nanjing University. The author has contributed to research in topics: Software bug & Deep learning. The author has an hindex of 19, co-authored 53 publications receiving 3065 citations.
Papers
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Journal ArticleDOI
Tri-training: exploiting unlabeled data using three classifiers
Zhi-Hua Zhou,Ming Li +1 more
TL;DR: Experiments on UCI data sets and application to the Web page classification task indicate that tri-training can effectively exploit unlabeled data to enhance the learning performance.
Journal ArticleDOI
Semi-supervised learning by disagreement
Zhi-Hua Zhou,Ming Li +1 more
TL;DR: An introduction to research advances in disagreement-based semi-supervised learning is provided, where multiple learners are trained for the task and the disagreements among the learners are exploited during the semi- supervised learning process.
Journal ArticleDOI
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
Ming Li,Zhi-Hua Zhou +1 more
TL;DR: Case studies on three medical data sets and a successful application to microcalcification detection for breast cancer diagnosis show that undiagnosed samples are helpful in building CAD systems, and Co-Forest is able to enhance the performance of the hypothesis that is learned on only a small amount of diagnosed samples by utilizing the available undiognosed samples.
Proceedings Article
Semi-supervised regression with co-training
Zhi-Hua Zhou,Ming Li +1 more
TL;DR: Experiments show that COREG can effectively exploit unlabeled data to improve regression estimates and is proposed as a co-training style semi-supervised regression algorithm.
Proceedings ArticleDOI
Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code
Hui-Hui Wei,Ming Li +1 more
TL;DR: Experiments on software clone detection benchmarks indicate that the CDLH approach is effective and outperforms the state-of-the-art approaches in software functional clone detection.