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东南大学张敏灵博士学术讲座预告

作者:DMIR    来自:    发表时间:2016-05-22    浏览量:797


报告时间201605月24日(周二)下午1500

报告地点:广工大学城校园工学一号馆725

报告题目:Binary relevance for multi-label learning

报告人:东南大学 张敏灵博士

报告人简介:

     张敏灵,东南大学计算机科学与工程学院教授。分别于2001年、2004年和2007年于南京大学计算机科学与技术系获学士、硕士和博士学位。主要研究领域为机器学习、数据挖掘。现任中国人工智能学会机器学习专委会秘书长、中国计算机学会人工智能与模式识别专委会委员等。担任《Frontiers of Computer Science》编委,《Machine Learning》、《软件学报》等客座编辑。应邀担任PRICAI16程序主席、IJCAI’15、ICDM’16、ACML’16等国际会议高级程序委员,以及AAAI’16、KDD’16、ICML’16等国际会议程序委员。获NSFC优秀青年科学基金(2012年度)、教育部“长江学者奖励计划”青年学者(2015年度)等。


报告摘要:

    In recent years, multi-label learning has become one of the important research topics in machine learning community. Binary relevance (BR) is arguably the most popular approach towards multi-label learning, which decomposes the multi-label learning problem into a number of independent binary classification problems, one per category. In view of the well-known weakness of BR, i.e. ignorance of label correlations, a number of enhanced versions of BR have been developed in recent years by endowing BR with the ability of label correlations exploitation. Nonetheless, in addition to label correlations exploitation, there are several factors which need to be considered to make BR-based approach work effectively. In this talk, I will introduce some of our recent progresses on BR-based multi-label learning.

 

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