%X 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.
%D 2004
%I University of Trento
%A Emanuela Bermani
%A Andrea Boni
%A Salvatore Caorsi
%A Massimo Donelli
%A Andrea Massa
%T A Multi-Source Strategy based on a Learning-by-Examples Technique for Buried Object Detection
%L elediasc12409