?!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">中北大学研究生第20191303期华彩讲坛预?研究生院
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中北大学研究生第20191303期华彩讲坛预?/SPAN>

发文旉:2019-11-26 08:58 点击量: 作?

讲题目Q?/span>Outlier Detection from High-dimensional and Distributed Data Sources

讲旉Q?019q?1?7日下?:10

讲地点Q?9402H

主讲人:张吉

主讲人简介:

澛_利亚南昆士兰大学计算机科学副教授Q终w教授职位,博士生导师,曾Q南昆士兰大学信息技术中心首席研I? IEEE高会员QACM会员Q澳大利亚奋q学者,昆士兰学者,加拿大Killam学者,W?届世界智能大会特邀专家Q美国密歇根州立大学、新加坡南洋理工大学和日本筑波大学访问客座教授。张吉教授获2011q度南昆士兰大学杰出研究奖和澛_利亚奋进奖。张吉教授的主要研究方向为大数据分析, 数据挖掘, 信息隐私保护及安全,计算{,张吉教授已在国际主要期刊和国际会议中发表研究论文150余篇Qƈ撰写个h专著1部及专著论文7?/span>

讲主要内容Q?/span>

In this talk, I will present some of our recent work on outlier detection. I will first talk about a technique for detecting outliers from large high-dimensional data streams. Computational Intelligence method based on Genetic Algorithm is developed to achieve efficient high-dimensional subspace search. Innovative fast convergence technique is also proposed to significant speed up the detection process. I will then introduce a technique for detecting outliers from multiple large distributed databases. It is able to effectively detect the so-called global outliers from distributed databases that are consistent with those produced by the centralized detection paradigm.

备注Q请教室前中部座位留给d单位学生


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