PhD Qualifying Examination


"Machine Learning Algorithms in Sequential Data and the Applications"

Mr. Dou Shen


Abstract:

Sequential data arises in many areas of science and engineering. This
paper introduces the tasks in sequential data analysis and then discusses
several popular models and algorithms for the tasks as well as their
applications. The methods and models include the sliding window method,
Markov chain, Hidden Markov Model, Linear Dynamic System, Conditional
Random Fields, Dynamic Bayes Networks, Stochastic Context-Free Grammars,
ARMA models and Independent Component Analysis. This paper also presents
the previous work on sequential data in the field of text mining and
information retrieval.  At the end of the paper, we point out the possible
future research directions.


Date:     		Monday, 12 December 2005

Time:                   11:00a.m.-1:00p.m.

Venue:                  Room 1403
			lifts 25-26

Committee Members:      Dr. Qiang Yang (Supervisor)
			Prof. Dik-Lun Lee (Chairperson)
			Dr. Dekai Wu
			Dr. Dit-Yan Yeung


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