Economic Analysis of Risky Projects Based on LS-SVM

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Risk management and its accurate analysis are very important for project management. RBF and MLP Neural Network Model are common methods of risk management and analysis, which are not accurate enough. In this paper a new method based on LS-SVM is introduced. Analytical models of risky projects are investigated and function approximation results are compared. Experimental results show that the regression analysis of risk based on LS-SVM method has higher prediction accuracy and better generalization ability.

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403-406

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June 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] A. Taskin, A. F. Guneri: Economic analysis of risky projects by ANNs, Applied Mathematics and Computation. Vol. 175. (2006), pp.171-181.

DOI: 10.1016/j.amc.2005.07.016

Google Scholar

[2] V. Vapnik: The nature of statistical learning theory, New York : springer - verlag, (2000).

Google Scholar

[3] C. Cortes, V. Vapnik: Support vector networks, Machine Learning. Vol. 20. (1995), pp.273-297.

DOI: 10.1007/bf00994018

Google Scholar

[4] Y. F. Wen: Materials experimental data analysis and application of support vector regression, Chongqing University Master's Thesis (2009), pp.22-32.

Google Scholar

[5] S. Ward, C. Chapman: Transforming project risk management into project uncertainty management, International Journal of Project Management. Vol. 21. (2003), p.97–105.

DOI: 10.1016/s0263-7863(01)00080-1

Google Scholar

[6] T. Raz, E. Michael: Use and benefits of tools for project risk management, International Journal of Project Management. Vol. 19. (2001), p.9–17.

DOI: 10.1016/s0263-7863(99)00036-8

Google Scholar

[7] Z. H. Zou, H. Y. Jiao: Based on least squares support vector regression for short-term water demand forecast, Water Supply and Drainage. Vol. 34. (2008), pp.328-331.

Google Scholar

[8] G. H. Yan, Y. S. Zhu: Support vector machine regression parameter selection method, Computer Engineering, Vol. 35. Supplement (2009), pp.218-220.

Google Scholar

[9] C. Vladimir, Y. Q. M: Practical Selection of SVM Parameters and Noise Estimation for SVM Regression, Neural Networks, Vol. 17. (2004), pp.113-126.

DOI: 10.1016/s0893-6080(03)00169-2

Google Scholar

[10] S. Sathiya, C. J. Keerthi: Asymptotic Behavior of Support Vector Machines with Gaussian Kernel, Neural Computation, Vol. 15. (2003), pp.1667-1689.

DOI: 10.1162/089976603321891855

Google Scholar