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    学术报告Isolation Set-Kernel and Its Application to Multi-Instance Learning
    发布时间:2019-05-15 14:40:36
    南京大学计算机软件新技术国家重点实验室        
    摘 要:
    In many real-world applications, objects are more effectively and naturally be represented as sets of points, e.g., treating an image as a set of patches; a galaxy cluster of individual glaxities. This treatment demands a way to measure similarity between two sets effectively in order to reach its full potential. As a result of a lack of effective set-similarity measures, most existing applications have been forced to create a single vector to represent the entire set.  In addition to be a better representation, an effective set-similarity measure offers to significantly reduce the time and space complexities of an algorithm that operates on points.  The proposed Isolation Set-Kernel is a better measure than existing Fisher kernel and Gaussian kernel-based set-kernels in terms of efficacy and efficiency. This talk reports its advantages in Multi-Instance Learning (MIL) using SVM classifiers.
    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 one of the creators of isolation techniques, mass-based similarity and kernels based on isolation mechanisms.
    时间:5月16日  10:30-11:30
    地点:计算机科学技术楼229室

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    友情链接:
    江苏省科学技术协会 中国计算机学会 南京大学 南京大学计算机科技与技术系 南京大学软件学院 东南大学计算机科学与工程学院 江苏经贸职业技术学院 南京信息职业技术学院 南京工业职业技术学院 江苏海事职业技术学院 常州信息职业技术学院 国网电力科学研究院 电子科技集团第28研究所 江南计算技术研究所 
       
     

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