eprintid: 571
rev_number: 6
eprint_status: archive
userid: 5
dir: disk0/00/00/05/71
datestamp: 2004-12-03
lastmod: 2013-06-28 14:14:24
status_changed: 2013-06-28 14:14:24
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Bermani, Emanuela
creators_name: Boni, Andrea
creators_name: Kerhet, Aliaksei
creators_name: Massa, Andrea
title: Kernels Evaluation of Svm-Based Estimators for Inverse Scattering Problems
ispublished: pub
subjects: TU
full_text_status: public
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.
date: 2004
date_type: published
institution: University of Trento
department: informaticat
refereed: FALSE
referencetext: 
[1] Bermani, E., Boni, A., Caorsi, S., Massa, A. "An Innovative Real-Time Tech-nique for Buried Object Detection"; IEEE Transactions on Geoscience and Remote Sensing , Vol. 41, No. 4, 927-931, 2003.
[2] Vapnik, V. N., The Nature of Statistical Learning Theory , Statistics for Engi-neering and Information Science, Springer Verlag, 2nd edition, 1999.
[3] Caorsi, S., Anguita, D., Bermani, E., Boni, A., Donelli, M., "A comparative study of nn and svm-based electromagnetic inverse scattering approaches toon-line detection of buried objects"; ACES journal , Vol. 18, No. 2, 2003.
[4] Cristianini, N., Shawe-Taylor, J., An Introduction to Support Vector Machines,Cambridge University Press, 2000.
[5] Scholkopf, B., Smola, A. J., Learning with Kernels, MIT Press, Cambridge,MA, 2002.
[6] Bertsekas, D. P., Constrained Optimization and Lagrange Multipliers, AcademicPress, New York, 1982.
[7] Aizerman, M. A., Braverman, E. M., Rozonoer, L. I., "Theoretical Foundationsof the Potential Function Method in Pattern Recognition Learning"; Automa-tion and Remote Control , Vol. 25, 821-837, 1964.
[8] Platt, J., "Fast Training of Support Vector Machines Using Sequential MinimalOptimization", in B. Scolkopf, C. Burges, A. Smola (Eds.), Advances in KernelMethods"; Support Vector Learning , MIT Press, 1999.15
citation:   Bermani, Emanuela and Boni, Andrea and Kerhet, Aliaksei and Massa, Andrea  (2004) Kernels Evaluation of Svm-Based Estimators for Inverse Scattering Problems.  [Technical Report]     
document_url: http://www.eledia.org/students-reports/571/1/report_with_copertina.pdf