@misc{elediasc12571,
            year = {2004},
          author = {Emanuela Bermani and Andrea Boni and Aliaksei Kerhet and Andrea Massa},
           title = {Kernels Evaluation of Svm-Based Estimators for Inverse Scattering Problems},
             url = {http://www.eledia.org/students-reports/571/},
        keywords = {Support Vector Machines, Statistical Learning, Microwave Inverse Scattering, Model Selection.},
        abstract = {Buried object detection by means of microwave-based sensing techniques is faced in biomedical imaging, mine detection etc. Whereas conventional methods used for such a problem consist in solving nonlinear integral equations, this work considers a recently proposed approach based on Support Vector Machines, the techniques that proved to be theoretically justi?ed and effective in real world domains. Simulation is carried out on synthetic data generated by Finite Element code and a PML technique; noisy environments are considered as well. Results obtained for cases of polynomial and Gaussian kernels are presented and discussed.}
}