eprintid: 490
rev_number: 10
eprint_status: archive
userid: 5
dir: disk0/00/00/04/90
datestamp: 2011-05-10
lastmod: 2018-02-27 10:04:20
status_changed: 2013-07-01 11:18:34
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Boni, Andrea
creators_name: Conci, Massimo
creators_name: Massa, Andrea
creators_name: Piffer, Stefano
title: On The Use Of SVM For Electromagnetic Subsurface Sensing
ispublished: unpub
subjects: TU
full_text_status: public
abstract: In this paper, a classification approach for the real-time identification of “occupation” areas (instead of the detection of each subsurface object) in sub-surface sensing applications is applied. A suitable SVM-based strategy is developed for determining the probability of occurrence of buried targets and to define a “risk map” of the investigation domain. To assess the effectiveness of the proposed approach and to evaluate its robustness, selected numerical results related to a two-dimensional geometry are presented.
date: 2010-01
date_type: published
publisher: University of Trento
institution: University of Trento
department: informaticat
refereed: FALSE
referencetext: [1] IEEE Trans. Geosci. Remote Sens., Special Issue on: “New Advances in Subsurface Sensing: Systems, Modeling and Signal Processing,” vol. 39, Jun. 2001. [2] S. Caorsi, D. Anguita, E. Bermani, A. Boni, M. Donelli, and A. Massa, “A comparative study of NN and SVM-based electromagnetic inverse scattering approaches to on-line detection of buried objects,” J. Applied Computat. Electromagnetics Soc., vol. 18, pp. 1-11, 2003. [3] E. Bermani, A. Boni, S. Caorsi, and A. Massa, “An innovative real-time technique for buried object detection” IEEE Trans. Geosci. Remote Sens., vol. 41, pp. 927-931, 2003. [4] N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge, University Press, 2000. [5] K. Morik, P. Brockhausen, and T. Joachims, "Combining statistical learning with a knowledge-based approach: a case study in intensive care monitoring", Proc. 16th Int. Conf. Machine Learning, MIT Press., 1999. [6] J. Platt, "Fast training of support vector machines using sequential minimal optimization," in Advances in Kernel methods - Support Vector Learning, B. Scholkopf, C. J. C. Burges, and A. J. Smola (Eds.), MIT Press, 1999. [7] K. -R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, "An introduction to kernel-based learning algorithms," IEEE Trans. Neural Networks, vol. 12, pp. 181-201, Mar. 2001. [8] J. Platt, "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods," in Advances in Large Marging Classifiers, A. J. Smola, P. Bartlett, B. Scholkopf, D. Schuurmans (Eds.), MIT Press, 1999. [9] D. Anguita, S. Ridella, F. Rivieccio, and R. Zunino, " Hyperparameter Design Criteria for Support Vector Machines," Neurocomputing, vol. 55, pp. 109-134, 2003.
citation:   Boni, Andrea and Conci, Massimo and Massa, Andrea and Piffer, Stefano  (2010) On The Use Of SVM For Electromagnetic Subsurface Sensing.  [Technical Report]    (Unpublished)  
document_url: http://www.eledia.org/students-reports/490/1/DISI-11-278.C87.pdf