CRISTIANINI N, SHAWE-TAYLOR J.An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[M].New York:Cambridge University Press, 2000.
[5]
VAPNIK V N.Statistical Learning Theory[M].New York:Wiley-interscience, 1998.
SMOLA A, MURATA N, SCHOLKOPF B, et al.Asymptotically Optimal Choice of ε-loss for Support Vector Machines[C]//NIKLASSON L, BODEN M, ZIEMKE T.Proceedings of the 8th International Conference on Artificial Neural Networks.Berlin:Springer, 1998:105-110.
[8]
SCHOLKOPF B, BARTLETT P, SMOLA A, et al.Support Vector Regression with Automatic Accuracy Control[C]//NIKLASSON L, BODEN M, ZIEMKE T.Proceedings of the 8th International Conference on Artificial Neural Networks.Berlin:Springer, 1998:111-116.
[9]
CHERKASSKY V, MA Y.Selection of Meta-parameters for Support Vector Regression[C]//DORRONSORO J R.Proceedings of the International Conference on Artificial Neural Networks.Madrid:Springer, 2002:687-693.
[10]
LIU Kai-yun, QIAO Chun-sheng, TENG Wen-yan.Research on Nonlinear Time Sequence Intelligent Model Construction and Prediction of Slope Displacement by Using Support Vector Machine Algorithm[J].Chinese Journal of Geotechnical Engineering, 2004, 26(1):57-61.
[11]
FENG Xia-ting.Introduction of Intelligent Rock Mechanics[M].Beijing:Science Press, 2000.
[12]
JIANG Xin, WEI Yong-xing, QIU Yan-jun.Stability of Subgrade Embankment on Sloped Weak Ground[J].Journal of Traffic and Transportation Engineering, 2003, 3(1):30-34.
[13]
SHI Gang, WANG Jin-guo, ZHI Xi-lan, et al.Calculation Method of Foundation Bearing Capacity Based on Division in Loess Area for Highway Engineering[J].Journal of Traffic and Transportation Engineering, 2005, 5(4):48-52.
[14]
VAPNIK V N.The Nature of Statistical Learning Theory[M].New York:Springer Verlag, 1995.
[15]
FU Yi-xiang, LIU Zhi-qiang.Analytic Method and Application About Chaotic Slope Deformation Destruction Time-series[J].Journal of Wuhan University of Technology, 2003, 27(4):474-476.
[16]
CHERKASSKY V, MA Y.Practical Selection of SVM Parameters and Noise Estimation for SVM Regression[J].Neural Networks, 2004, 17(1):113-126.
[17]
CHALIMOURDA C, SCHOLKOPF B, SMOLA A.Experimentally Optimal V in Support Vector Regression for Different Noise Models and Parameter Settings[J].Neural Networks, 2004, 17(1):127-141.
