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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A Comparison of Shallow Decision Trees Under Real-Boost Procedure with Application to Landmine Detection Using Ground Penetrating Radar
verfasst von : Przemysław Klęsk, Mariusz Kapruziak, Bogdan Olech
Erschienen in: Artificial Intelligence and Soft Computing
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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.