@misc{elediasc12409,
            year = {2004},
          author = {Emanuela Bermani and Andrea Boni and Salvatore Caorsi and Massimo Donelli and Andrea Massa},
           month = {August},
           title = {A Multi-Source Strategy based on a Learning-by-Examples Technique for Buried Object Detection},
             url = {http://www.eledia.org/students-reports/409/},
        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.}
}