1. Bermani, E., S. Caorsi, and M. Raffetto, "An inverse scattering approach based on a neural network technique for the detection of dielectric cylinders buried in a lossy half-space," Progress In Electromagnetics Research, Vol. 26, 67-87, 2000.
doi:10.2528/PIER99052001 Google Scholar
2. Jin, L., C. L. Ruan, and L. Y. Chun, "Design E-plane bandpass filter based on EM-ANN model," Journal of Electromagnetic Waves and Applications, Vol. 20, No. 8, 1061-1069, 2006.
doi:10.1163/156939306776930259 Google Scholar
3. Lee, Y. and D. S. Filipovic, "ANN based electromagnetic models for the design of RF MEMS switches," IEEE Microwave and Wireless Components Letters, Vol. 15, 823-825, 2005.
doi:10.1109/LMWC.2005.859001 Google Scholar
4. Chen, K., C. Ho, and H. Shiau, "Application of support vector regression in forecasting international tourism demand," Tourism Management Research, Vol. 4, 81-97, 2004. Google Scholar
5. Vapnik, V., The Nature of Statistical Learning Theory, Springer- Verlag, 1995.
6. Wei, C., J. O. Chong, and S. S. Keerthi, "An improved conjugate gradient scheme to the solution of least squares SVM," IEEE Trans. Neural Network, Vol. 6, 498-501, 2005. Google Scholar
7. Shevade, S. K., S. S. Keerthi, C. Bhattacharyya, and K. R. K. Murthy, "Improvements to the SMO algorithm for SVM regression," IEEE Trans. Neural Network, Vol. 11, 1188-1193, 2000.
doi:10.1109/72.870050 Google Scholar
8. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of SVM based estimatiors for inverse scattering problems," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005.
doi:10.2528/PIER04090801 Google Scholar
9. Scholkopf, B., A. J. Smola, R. Williamson, and P. Bartlett, "New support vector algorithms," NeuroCoLT2TechnicalReportsSeries:NC2-TR-1998-031, 2-031, 1998. Google Scholar
10. Chang, C. C. and C. J. Lin, "LIBSVM: a library for support vector machines," System documention, 2004. Google Scholar
11. Scheaffer, R. and J. Mcclave, Statistics for Engineers, Duxbury Press, 1982.