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Reduced Support Vector Selection by Linear Programs

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Book cover Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence (IWANN 2001)

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Abstract

The problem of selecting a minimal number of data points required to completely specify a nonlinear separating hyperplane classifier is formulated as a concave minimization problem and solved using a linear program. A comparison of the prediction errors for several rule extraction methods shows a good compromise between complexity of the classifier and the errors.

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Author information

Authors and Affiliations

  1. Dept. of Math., King’s Coll., Strand, London, WC2R 2LS, UK

    Winfried A. Fellenz

Authors
  1. Winfried A. Fellenz
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, s/n., Madrid, 28040, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Fellenz, W.A. (2001). Reduced Support Vector Selection by Linear Programs. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_81

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  • DOI: https://doi.org/10.1007/3-540-45720-8_81

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42235-8

  • Online ISBN: 978-3-540-45720-6

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