| 講演者： |
Shaoning Pang (Auckland University of Technology) |
| 題目： |
Learning Linear Discriminant Analysis in Present and Future Network Environment
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| 概要： |
Linear Discriminant Analysis (LDA) has been researched for the computational
intelligence on network security applications. In the context of present and
future network communication and security, this talk introduces a series of LDA
new developments, where an LDA model is enabled to be learned either in one
batch session, or incrementally by Incremental LDA (ILDA), or through LDA
eigenspace merging (LDA Merging); For parallel computing, a global LDA can be
learned cooperatively by a group of regional LDA agents with their knowledge
shared each other while learning (mILDA); In a special case, a created LDA can
be renovated by splitting LDA eigenspace with a minimum processing on the raw
data instance (LDA Splitting); For even higher performance computing, LDA can be
set to be learned only on fewer selected curiosity instances (cILDA), or by
multiple cILDA agents in a competitive and cooperative learning manner (mcILDA).
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| 講演者紹介： |
Dr. Shaoning Pang holds BSc in physics, MSc in electronic engineering, and PhD
in computer science. From 2001 to 2003, he worked as a research associate in
Pohang University of Science and Technology, South Korea. His research interests
include SVM Aggregating Intelligence, Incremental Machine Learning,
Bioinformatics, and Industrial Business Intelligence Systems and Strategies. He
has been serving as a program member and session chair for several international
conferences including ISNN, ICONIP. ICNNSP. He was a best paper winner of IEEE
ICNNSP 2003. He is currently acting as a paper reviewer for IEEE Transaction on
SMC-Part B, Image and Vision Computing Journal and Pattern Recognition Letter,
and a guest editor for International Journal of Computers, Systems and Signals.
Dr. Pang is a Senior Member of IEEE, and a Member of IEICE, and ACM.
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