2018年4月13日学术报告（吴佳，国际数据挖掘顶级期刊ACMTKDD副主编）

2018年4月13日学术报告（吴佳，国际数据挖掘顶级期刊ACMTKDD副主编）

类别：未知发布人：admin 浏览次数：次发布时间：2018-04-10 11:24

报告题目： Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning以及“武大-麦考瑞大学计算机学科双博士学位项目”招生启动事项
报告时间：    2018年4月13日上午10:30（周五）
报告地点：   计算机学院B403
报告人：      Jia Wu（吴佳）
报告人国籍：中国
报告人单位：澳大利亚麦考瑞大学

报告人简介： Jia Wu（吴佳）：国际数据挖掘顶级期刊ACM Transactions on Knowledge Discovery Data（TKDD）副主编。澳大利亚麦考瑞大学计算机学院讲师，博士、IEEE会员。主要研究领域为数据挖掘、机器学习、人工智能，及其在商业、工业、生物信息学、医疗信息学等领域的应用。迄今，在国际学术期刊和会议上共发表论文100多篇, 包括IEEE Transactions on Knowledge and Data Engineering、IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Cybernetics、ACM Transactions on Knowledge Discovery Data、IEEE Transactions on Systems, Man, and Cybernetics: Systems、Pattern Recognition、IJCAI、AAAI、ICDM、SDM、CIKM等。指导学生曾获得2017顶级国际神经网络大会International Joint Conference on Neural Networks (IJCNN) 最佳学生论文奖、2014顶级国际数据挖掘会议International Conference on Data Mining的最佳论文提名奖。现任SCI、JCR一区期刊Journal of Network and Computer Applications副主编和Complexity Journal (SCI: 3.514)客座主编。担任国际顶级神经网络大会2016、2017、2018 International Joint Conference on Neural Networks的专题分会主席 (Special Session Chair)、2018 International Conference on Applications and Techniques in Information Security的程序委员会主席(Program CommitteeChair)、顶级人工智能国际会议International Joint Conference on Artificial Intelligence, IJCAI 2017和2018的高级程序委员 (Senior Program Committee)，顶级国际学术会议的程序委员 (Program Committee), 包括IJCAI、AAAI、KDD、ICDM、SDM、CIKM等。

报告摘要：In traditional multi-instance learning (MIL), instances are typically represented by using a single feature view. As MIL becoming popular in domain specific learning tasks, aggregating multiple feature views to represent multi-instance bags has recently shown promising results, mainly because multiple views provide extra information for MIL tasks. Nevertheless, multiple views also increase the risk of involving redundant views and irrelevant features for learning. To this end, we formulate a new cross-view feature selection problem that aims to identify the most representative features across all feature views for MIL. To achieve the goal, we design a new optimization problem by integrating both multi-view representation and multi-instance bag constraints. The solution to the objective function will ensure that the identified top-m features are the most informative ones across all feature views. Experiments on two real-world applications demonstrate the performance of the cross-view feature selection for content-based image retrieval and social media content recommendation.

邀请人：杜博教授

上一篇：2018年4月10日学术报告（Paul de Vrieze, Bournemouth University）

下一篇：2018年4月18日学术报告（黄如花，武汉大学珞珈特聘教授）