Robustness Analysis of Novel ε-Support Vector Regression

Yuan Lv; Zhong Gan

doi:10.4028/www.scientific.net/AMM.543-547.2049

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Robustness Analysis of Novel ε-Support Vector...

Robustness Analysis of Novel ε-Support Vector Regression

Abstract:

The key to the robust ε-support vector regression algorithm is searching for the optimal regression hyper plane while data with disturbance in the X-direction. In the paper, the optimal regression hyper plane and the optimal separating hyper plane are compared and analyzed. By means of Kolmogorov test, it is can be deduced that the testing errors of the robust ε-support vector regression experiments follow normal distribution. The result demonstrates that the algorithm has good forecast accuracy and high robustness.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

2049-2052

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.2049

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Online since:

March 2014

Authors:

Yuan Lv*, Zhong Gan

Keywords:

Kolmogorov Test, Optimal Regression Hyper Plane, Robustness, ε-Support Vector Regression

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References

[1] A. L. Soyster: Convex Programming with Set-inclusive Constraints and Applications to Inexact Linear Programming, Oper. Res. , (1973) No. 21, p.1154.

DOI: 10.1287/opre.21.5.1154

Google Scholar

[2] L. El-Ghaoui and H. Lebret: Robust Solutions to Least-square Problems to Uncertain Data Matrices, SIAM J. Matrix Anal, (1997) No. 18, p.1035.

DOI: 10.1137/s0895479896298130

Google Scholar

[3] L. El-Ghaoui, F. Oustry and H. Lebret: Robust Solutions to Semi-definite Programs, SIAM J. Optim. , (1998) No. 9, p.33.

DOI: 10.1137/s1052623496305717

Google Scholar

[4] A. Ben-Tal and A. Nemirovski: Robust Convex Optimization, Math. Oper. Res. , (1998) No. 23, p.769.

Google Scholar

[5] A. Ben-Tal and A. Nemirovski: Robust Solutions to Uncertain Programs, Oper. Res. Letters, (1999) No. 25, p.1.

DOI: 10.1016/s0167-6377(99)00016-4

Google Scholar

[6] S. Melvyn: Robust Optimization, (Phd. Thesis, June 2004).

Google Scholar

[7] L. Breiman. Statistical Modeling: The Two Cultures, Statistical Science, Vol. 16 (2001) No. 3, p.199.

Google Scholar

[8] Q. Le and S. Bengio: Noise Robust Discriminative Models [EB/OL]. [2003-09-01]. www. idiap. ch/fip/reports/2003/n03-40. ps. gz.

Google Scholar

[9] N.Y. Deng, Y.J. Tian and C.H. Zhang: Support Vector Machines – Optimization Based Theory, Algorithms and Extensions (CRC Press, 2013).

Google Scholar

[10] V.N. Vapnik: The Nature of Statistical Learning Theory (New York: Springer, 1999).

Google Scholar

[11] Mathematical Statistics Writing Group: Mathematical Statistics, (China: Northwestern Polytechnical University Press, 1998).

Google Scholar