eprintid: 409
rev_number: 6
eprint_status: archive
userid: 5
dir: disk0/00/00/04/09
datestamp: 2004-09-01
lastmod: 2013-07-04 11:46:05
status_changed: 2013-07-04 11:46:05
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Bermani, Emanuela
creators_name: Boni, Andrea
creators_name: Caorsi, Salvatore
creators_name: Donelli, Massimo
creators_name: Massa, Andrea
title: A Multi-Source Strategy based on a Learning-by-Examples Technique for Buried Object Detection
ispublished: inpress
subjects: TU
full_text_status: public
abstract: In the framework of buried object detection and subsurface sensing, some of the main difficulties in the reconstruction process are certainly due to the aspect-limited nature of available measurement data and to the requirement of an on-line reconstruction. To limit these problems, a multi-source (MS) learning-by-example (LBE) technique is proposed in this paper. In order to fully exploit the more attractive features of the MS strategy, the proposed approach is based on a support vector machine (SVM). The effectiveness of the MS-LBE technique is evaluated by comparing the achieved results with those obtained by means of a previously developed single-source (SS) SVM-based procedure for an ideal as well as a noisy enviroment.
date: 2004-08
date_type: published
institution: University of Trento
department: informaticat
refereed: FALSE
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citation:   Bermani, Emanuela and Boni, Andrea and Caorsi, Salvatore and Donelli, Massimo and Massa, Andrea  (2004) A Multi-Source Strategy based on a Learning-by-Examples Technique for Buried Object Detection.  [Technical Report]    (In Press)  
document_url: http://www.eledia.org/students-reports/409/1/DIT-04-067.pdf