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关于举办Kai Ming Ting教授学术讲座的通知--2018西邮学术讲座第七十四讲

2018-10-10 

各部门、各单位:  

应我校邀请,Kai Ming Ting教授(Federation University Australia)将于10月11日来我校做专题学术讲座,欢迎全校师生参加!报告的具体安排如下:  

报告主题:Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees

人:Kai Ming Ting博士、教授

工作单位:Federation University Australia

报告时间:10月11日(星期四)下午3点

报告地点:自动化学院楼110会议室

内容提要:This talk reports an investigation on an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The SENC problem can be decomposed into three subproblems: detecting emerging new classes, classifying known classes, and updating models to integrate each new class as part of known classes. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The proposed method employs completely-random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. The completely-random trees are used as a single common core to solve all three subproblems in SENC: unsupervised learning, supervised learning, and model update on data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods.

报告人简介:After receiving his PhD from the University of Sydney, Kai Ming Ting had worked at the University of Waikato, Deakin University and Monash University. He joins Federation University Australia since 2014. He had previously held visiting positions at Osaka University, Nanjing University, and Chinese University of Hong Kong. His current research interests are in the areas of mass estimation, mass-based dissimilarity, anomaly detection, ensemble approaches, data streams, data mining and machine learning in general. He has served as a program committee co-chair for the Twelfth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2008). He was a member of the program committee for a number of international conferences including ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, and International Conference on Machine Learning. He has received research funding from Australian Research Council, US Air Force of Scientific Research (AFOSR/AOARD), Toyota InfoTechnology Center, and Australian Institute of Sports. Awards received include the Runner-up Best Paper Award in 2008 IEEE ICDM (for Isolation Forest), and the Best Paper Award in 2006 PAKDD. He is the creator of isolation techniques, mass estimation and mass-based dissimilarity.

特此通知。  

科研处  

自动化学院  

2018年10月9日  


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