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
Link To Document :
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