dai-ensembleparameter-2003.pdf (171.22 kB)
Ensemble parameter estimation for graphical models
conference contribution
posted on 2003-01-01, 00:00 authored by Yiqing Tu, Gang LiGang Li, Honghua DaiParameter Estimation is one of the key issues involved in the discovery of graphical models from data. Current state of the art methods have demonstrated their abilities in different kind of graphical models. In this paper, we introduce ensemble learning into the process of parameter estimation, and examine ensemble parameter estimation methods for different kind of graphical models under complete data set and incomplete data set. We provide experimental results which show that ensemble method can achieve an improved result over the base parameter estimation method in terms of accuracy. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.
History
Title of proceedings
InTech'03 : Proceedings of the Fourth International Conference on Intelligent Technologies 2003Event
International Conference on Intelligent Technologies (4th : 2003, Thailand)Pagination
1 - 11Publisher
Chiang Mai University, Institute for Science and Technology Research and DevelopmentLocation
Chiang Mai, ThailandPlace of publication
Chiang Mai, ThailandStart date
2003-12-17End date
2003-12-19ISBN-13
9789746581516ISBN-10
9746581511Language
engNotes
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E1 Full written paper - refereedEditor/Contributor(s)
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