Abstract
An application of Ground Penetrating Radar to landmine detection is presented. Using our prototype GPR system, we collect high-resolution 3D images, so called C-scans. By sampling 3D windows from C-scans, we generate large data sets for learning. We focus on experimentations with different recipes for growing shallow decision trees under the real-boost procedure. A particular attention is paid to the exponential criterion working as impurity function, in comparison to well known impurities. In the light of a theoretical bound on true error, driven from the properties of boosting, we check how greedy learning approaches translate in practice (for our GPR data) onto test error measures.
This work was partially financed by the Ministry of Science and Higher Education in Poland (R&D project no. 0 R00 0091 12, agreement signed on 30.11.2010).
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Klęsk, P., Kapruziak, M., Olech, B. (2015). A Comparison of Shallow Decision Trees Under Real-Boost Procedure with Application to Landmine Detection Using Ground Penetrating Radar. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_40
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DOI: https://doi.org/10.1007/978-3-319-19324-3_40
Publisher Name: Springer, Cham
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