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Notice of Retraction
Application of least squares support vector regression in network flow forecasting

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2 Author(s)
Guo Hebin ; Northern Beijing Vocational Educ. Inst., Beijing, China ; Guan Xiaoqing

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Accurate forecasting of network flow can control network congestion effectively and improve network performance. Least squares support vector regression is competent method to solve the non-linear problem and solve the problems of over-fitting and local minimum. Least squares support vector regression is adopted to predict network flow in the paper. Network flow data with 70 data points are adopted to research the forecasting ability of the proposed method in the paper compared with other methods. The mean relative error of LS-SVR is 1.45, and the mean relative error of BP neural network is 2.54. It is indicated that the LS-SVR forecasting model is better than BP neural network.

Published in:

Computer Engineering and Technology (ICCET), 2010 2nd International Conference on  (Volume:7 )

Date of Conference:

16-18 April 2010