DocumentCode
2283509
Title
A Learning Process Using SVMs for Multi-agents Decision Classification
Author
Xiao, Yanshan ; Deng, Feiqi ; Liu, Bo ; Liu, Shouqiang ; Luo, Dan ; Liang, Guohua
Author_Institution
Fac. of Inf. Technol., Univ. of Technol., Sydney, NSW
Volume
3
fYear
2008
fDate
9-12 Dec. 2008
Firstpage
583
Lastpage
586
Abstract
In order to resolve decision classification problem in multiple agents system, this paper first introduces the architecture of multiple agents system. It then proposes a support vector machines based assessment approach, which has the ability to learn the rules form previous assessment results from domain experts. Finally, the experiment are conducted on the artificially dataset to illustrate how the proposed works, and the results show the proposed method has effective learning ability for decision classification problems.
Keywords
decision making; learning (artificial intelligence); multi-agent systems; pattern classification; support vector machines; learning process; multiagent decision classification; support vector machine; Australia; Automation; Data mining; Information technology; Intelligent agent; Management training; Multiagent systems; Risk management; Support vector machine classification; Support vector machines; multi-agent support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
Conference_Location
Sydney, NSW
Print_ISBN
978-0-7695-3496-1
Type
conf
DOI
10.1109/WIIAT.2008.430
Filename
4740848
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