Title :
Predicting the Total Workload in Telecommunications by SVMs
Author :
Zhu, Mingfang ; Tang, Changjie ; Dai, Shucheng ; Xiang, Yong ; Qiao, Shaojie ; Yu, Chen
Author_Institution :
Sch. of Comput. Sci., Sichuan Univ., Chengdu
Abstract :
As a learning mechanic, support vector machine (SVMs) has been studied and applied in a wide area. This study deals with the special futures of SVM in predicting the total workload in telecommunication. The contributions include: (a) Building a predicted model of the total workload in telecommunications and predicting using it; (b)Analyzing the parameter of support vector regression(SVRs) which influence performance of SVRs. (c) Experiments demonstrate that SVM in this paper outperforms the others methods in this area.
Keywords :
data mining; support vector machines; telecommunication computing; SVM; support vector machine; support vector regression; telecommunications; total workload; Artificial neural networks; Computer science; Error correction; Machine learning; Predictive models; Risk management; Support vector machine classification; Support vector machines; Telecommunication computing; Time series analysis;
Conference_Titel :
Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on
Conference_Location :
Dalian, Liaoning
Print_ISBN :
978-0-7695-3161-8
Electronic_ISBN :
978-0-7695-3161-8
DOI :
10.1109/ICICIC.2008.427
