eprintid: 359
rev_number: 6
eprint_status: archive
userid: 5
dir: disk0/00/00/03/59
datestamp: 2011-08-04
lastmod: 2013-06-28 12:11:52
status_changed: 2013-06-28 12:11:52
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Benedetti, Manuel
creators_name: Donelli, Massimo
creators_name: Franceschini, Davide
creators_name: Rosani, Andrea
creators_name: Boni, Andrea
creators_name: Massa, Andrea
title: Recent Advances on the Use of Kernel-Based Learning-By-Examples Techniques for Electromagnetic Subsurface Sensing
ispublished: pub
subjects: TU
full_text_status: public
abstract: Electromagnetic approaches based on learning-rom-samples(LFS) techniques [1] have been proposed for the on-line [after the learning process (or training phase) performed once and off-line] detection of subsurface objects.
date: 2011-01
date_type: published
institution: University of Trento
department: informaticat
refereed: FALSE
referencetext: [1] E. Bermani, A. Boni, S. Caorsi, and A. Massa, “An innovative real-time technique for buried object detection,” IEEE Trans. on Geoscience and Remote Sensing., vol. 41, pp. 927-931, Apr. 2003. 
[2] S. Caorsi, E. Bermani, A. Boni, M. Conci, M. Donelli, and A. Massa, “Learning-by-examples strategies for sub-surface imaging: from regression to classification approach,” PIERS2003, USA, p. 118, Oct. 2003. [3] S. Caorsi, E. Bermani, A. Boni, M. Donelli, and A. Massa, “On the effectiveness of kernel based learning by examples techniques for electromagnetic subsurface sensing,” PIERS2004, Italy, p. S3.02, Mar. 2004.
citation:   Benedetti, Manuel and Donelli, Massimo and Franceschini, Davide and Rosani, Andrea and Boni, Andrea and Massa, Andrea  (2011) Recent Advances on the Use of Kernel-Based Learning-By-Examples Techniques for Electromagnetic Subsurface Sensing.  [Technical Report]     
document_url: http://www.eledia.org/students-reports/359/1/DISI-11-266.C99.pdf