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科研成果201807期 Isolation Kernel and Its Effect on SVM

16

2018-10

201807期 Isolation Ke

来源:系统管理员    浏览次数:5

 

题目:Isolation Kernel and Its Effect on SVM

主讲嘉宾: Kai Ming Ting

时间:2018831日下午  11:00-12:00 PM

地点:深圳大学南校区计算机与软件学院623会议室


Abstract:
This talk reports a work on data dependent kernels that are derived
directly from data. Data dependent kernels have been an outstanding issue for about
two decades which hampered the development of kernel-based
methods. We introduce Isolation Kernel which is solely dependent
on data distribution, requiring neither class information nor explicit
learning. In contrast, existing data dependent kernels rely heavily on 
class information and explicit learning. We show that Isolation Kernel approximates
well to a data independent kernel function called Laplacian kernel
under uniform density distribution. With this revelation, Isolation
Kernel can be viewed as a data dependent kernel that adapts a data
independent kernel to the structure of a dataset. We also provide
a reason why the proposed new data dependent kernel enables
SVM (which employs a kernel through other means) to improve its
predictive accuracy. 

Brief Bio:
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 or data dependent similarity, 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-based similarity and isolation kernel.

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