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Symbolic Knowledge Extraction from Support Vector Machines: A Geometric Approach

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5507))

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Abstract

This paper presents a new approach to rule extraction from Support Vector Machines (SVMs). SVMs have been applied successfully in many areas with excellent generalization results; rule extraction can offer explanation capability to SVMs. We propose to approximate the SVM classification boundary by solving an optimization problem through sampling and querying followed by boundary searching, rule extraction and post-processing. A theorem and experimental results then indicate that the rules can be used to validate the SVM with high accuracy and very high fidelity.

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Author information

Authors and Affiliations

  1. City University, London, UK

    Lu Ren & Artur d’ Avila Garcez

Authors
  1. Lu Ren
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  2. Artur d’ Avila Garcez
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Editor information

Editors and Affiliations

  1. Network Design and Research Center, Kyushu Institute of Technology,, 680-4, Kawazu, Iizuka,, 820-8502, Fukuoka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street,, 1142, Auckland, New Zealand

    George Coghill

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© 2009 Springer-Verlag Berlin Heidelberg

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Ren, L., Garcez, A.d.A. (2009). Symbolic Knowledge Extraction from Support Vector Machines: A Geometric Approach. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_41

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  • DOI: https://doi.org/10.1007/978-3-642-03040-6_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03039-0

  • Online ISBN: 978-3-642-03040-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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