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Hierarchical SVM Classification for Localization in Multilevel Sensor Networks

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Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

We show that the localization problem for multilevel wireless sensor networks (WSNs) can be solved as a pattern recognition with the use of the Support Vector Machines (SVM) method. In this paper, we propose a novel hierarchical classification method that generalizes the SVM learning and that is based on discriminant functions structured in such a way that it contains the class hierarchy. We study a version of this solution, which uses a hierarchical SVM classifier. We present experimental results the hierarchical SVM classifier for localization in multilevel WSNs.

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Authors and Affiliations

  1. Institute of Computer Science, Jagiellonian University, ul. Nawojki 11, 30-072, Cracow, Poland

    Jerzy Martyna

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  1. Jerzy Martyna
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Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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Martyna, J. (2008). Hierarchical SVM Classification for Localization in Multilevel Sensor Networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_61

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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