时间：2010年3月8号（星期一）下午2:30

地点：四层报告厅

报告人：Dr. Xingquan Zhu, University of Technology, Sydney

摘要：In this talk, I will summarize a number of steam data mining problems, Active Labeling, Cleansing, and Vague Learning, we have addressed in recent years. For active labeling, we consider that labeling all stream data is expensive and impractical, and our objective is to label a small portion of stream data from which a model is derived to predict newly arrived instances as accurate as possible. For data streams containing incorrectly labeled training samples, we propose a Maximum Variance Margin principle to accurately identify and remove mislabeled data, such that the prediction models built from the cleansed streams can be more accurate than the ones trained from the raw noisy streams. For vague learning in data streams, we allow users to label instance groups, instead of single instances, as positive samples for learning. Experimental results on synthetic and real-world data demonstrate the performances of the proposed efforts in comparison with other simple approaches.

报告人简介：Xingquan Zhu received his PhD degree in Computer Science from Fudan University, Shanghai China, in 2001. He is currently an Associate Professor of the Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia. Before joining the UTS, he was a tenure track Assistant Professor in the Department of Computer Science & Engineering, Florida Atlantic University, Boca Raton FL, USA, and a Research Assistant Professor in the Department of Computer Science, University of Vermont, Burlington VT, USA. Since 2000, he has published more than 100 referred journal and conference proceedings papers in these areas. Dr. Zhu is an Associate Editor of the IEEE Transactions on Knowledge and Data Engineering (2009- ), and a Program Committee Co-Chair for the 9th International Conference on Machine Learning and Applications (ICMLA 2010).