[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