relation: http://www.eledia.org/students-reports/409/
title: A Multi-Source Strategy based on a Learning-by-Examples Technique for Buried Object Detection
creator: Bermani, Emanuela
creator: Boni, Andrea
creator: Caorsi, Salvatore
creator: Donelli, Massimo
creator: Massa, Andrea
subject: TU Technical Reports and Publications
description: 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
type: Technical Report
type: NonPeerReviewed
format: text
language: en
identifier: http://www.eledia.org/students-reports/409/1/DIT-04-067.pdf
identifier:   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)