Boosting feed-forward neural network for internet traffic prediction

Hang Hang Tong, Chong Rong Li, Jing Rui He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Internet traffic prediction plays a fundamental role in network design, management, control, and optimization. The self-similar and non-linear nature of network traffic makes high accurate prediction difficult In this paper, boosting is introduced into traffic prediction by considering it as a classical regression problem. A new scheme together with its adaptive version is proposed to update weight distribution. The new scheme controls the update rate by a parameter, while its adaptive version introduces no extra parameter and is adaptive to the training error of basic regressors and the current iteration number. Experimental results on real network traffic which exhibits both self-similarity and non-linearity demonstrate the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationProceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages3129-3134
Number of pages6
StatePublished - Nov 2 2004
EventProceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China
Duration: Aug 26 2004Aug 29 2004

Publication series

NameProceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume5

Other

OtherProceedings of 2004 International Conference on Machine Learning and Cybernetics
CountryChina
CityShanghai
Period8/26/048/29/04

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Keywords

  • Boosting
  • Feed-Forward Neural Network (FFNN)
  • Non-linear
  • Regression
  • Self-similar
  • Traffic Prediction

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Tong, H. H., Li, C. R., & He, J. R. (2004). Boosting feed-forward neural network for internet traffic prediction. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics (pp. 3129-3134). (Proceedings of 2004 International Conference on Machine Learning and Cybernetics; Vol. 5).