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Progress In Electromagnetics Research
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KERNELS EVALUATION OF SVM-BASED ESTIMATORS FOR INVERSE SCATTERING PROBLEMS

By E. Bermani, A. Boni, A. Kerhet, and A. Massa

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Abstract:
Buried ob ject detection by means of microwave-based sensing techniques is faced in biomedical imaging, mine detection, and many other practical tasks. Whereas conventional methods used for such a problem consist in solving nonlinear integral equations, this article considers a recently proposed learning by examples approach [1] based on Support Vector Machines, the techniques that proved to be theoretically justified and effective in real world domains. The article considers the approach performance for two different kernel functions: Gaussian and polynomial. The obtained results demonstrate that using polynomial kernels along with slightly sophisticated model selection criterion allow to outperform the Gaussian kernels. Simulations have been carried out for synthetic data generated by Finite Element code and a PML technique; noisy environments are considered as well. The results obtained by means of polynomial and Gaussian kernels are presented and discussed.

Citation: (See works that cites this article)
E. Bermani, A. Boni, A. Kerhet, and A. Massa, "Kernels Evaluation of SVM-Based Estimators for Inverse Scattering Problems," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005.
doi:10.2528/PIER04090801
http://www.jpier.org/PIER/pier.php?paper=0409081

References:
1. Bermani, E., A. Boni, S. Caorsi, and A. Massa, "An innovative real-time technique for buried ob ject detection," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 4, 927-931, 2003.
doi:10.1109/TGRS.2003.810928

2. Vapnik, V. N., The Nature of Statistical Learning Theory, Statistics for Engineering and Information Science, 2nd edition, Springer Verlag, 1999.

3. Caorsi, S., D. Anguita, E. Bermani, A. Boni, and M. Donelli, "A comparative study of nn and svm-based electromagnetic inverse scattering approaches to on-line detection of buried ob jects," ACES Journal, Vol. 18, No. 2, 2003.

4. Cristianini, N. and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, 2000.

5. Schölkopf, B. and A. J. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 2002.

6. Bertsekas, D. P., Constrained Optimization and Lagrange Multipliers, Academic Press, New York, 1982.

7. Aizerman, M. A., E. M. Braverman, and L. I. Rozonoer, "Theoretical foundations of the potential function method in pattern recognition learning," Automation and Remote Control, Vol. 25, 821-837, 1964.

8. Platt, J., "Fast training of support vector machines using sequential minimal optimization," Advances in Kernel Methods — Support Vector Learning, 1999.

9. Lin, C.-J., "Asymptotic convergence of an SMO algorithm without any assumptions," IEEE Trans. on Neural Networks, Vol. 13, No. 1, 248-250, 2002.
doi:10.1109/72.977319

10. Smola, A., B. Schölkopf, R. Williamson, and P. Bartlett, "New support vector algorithms," Neural Computation, Vol. 12, No. 5, 1207-1245, 2000.
doi:10.1162/089976600300015565

11. Chang, C.-C. and Ch.-J. Lin, Libsvm: A Library for Support Vector Machines, May 2003., 2003.

12. Hastie, T., R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Springer, New York, 2001.

13. Anguita, D., S. Ridella, F. Rivieccio, and R. Zunino, "Hyperparameter design criteria for support vector classifiers," Neurocomputing, Vol. 55, No. 9, 109-134, 2003.
doi:10.1016/S0925-2312(03)00430-2

14. Hsu, Ch.-W., Ch.-Ch. Chang, and Ch.-J. Lin, "A practical guide to support vector classification," Department of Computer Science and Information Engineering, No. 7, 2003.

15. Bermani, E., A. Boni, S. Caorsi, M. Donelli, and A. Massa, "A multi-source strategy based on a learning-by-examples technique for buried ob ject detection," PIER Journal, Vol. 48, 185-200, 2004.
doi:10.2528/PIER03110701


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