Contents Intro.

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).

Details

Publication year

2010

Form

電子化映像資料(1時間06分56秒)

Series title

情報科学研究科・ゼミナール講演 ; 平成22年度

Note

Auckland University of Technology

講演日: 平成22年12月22日

講演場所: 情報科学研究科大講義室L1

Country of publication

Japan

Title language

English (eng)

Language of texts

English (eng)

Author information

Pang, Shaoning