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2004-06-22
A Multi-Source Strategy Based on a Learning-by-Examples Technique for Buried Object Detection
By
, Vol. 48, 185-200, 2004
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) SVMbased procedure for an ideal as well as a noisy environment.
Citation
Emanuela Bermani, Andrea Boni, Salvatore Caorsi, Massimo Donelli, and Andrea Massa, "A Multi-Source Strategy Based on a Learning-by-Examples Technique for Buried Object Detection," , Vol. 48, 185-200, 2004.
doi:10.2528/PIER03110701
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