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Convex Perceptrons

  • Daniel García
  • Ana González
  • José R. Dorronsoro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

Statistical learning theory make large margins an important property of linear classifiers and Support Vector Machines were designed with this target in mind. However, it has been shown that large margins can also be obtained when much simpler kernel perceptrons are used together with ad–hoc updating rules, different in principle from Rosenblatt’s rule. In this work we will numerically demonstrate that, rewritten in a convex update setting and using an appropriate updating vector selection procedure, Rosenblatt’s rule does indeed provide maximum margins for kernel perceptrons, although with a convergence slower than that achieved by other more sophisticated methods, such as the Schlesinger–Kozinec (SK) algorithm.

Keywords

Support Vector Machine Large Margin Breast Cancer Dataset Weight Update Norm Margin 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniel García
    • 1
  • Ana González
    • 1
  • José R. Dorronsoro
    • 1
  1. 1.Dpto. de Ingeniería Informática and Instituto de Ingeniería del ConocimientoUniversidad Autónoma de MadridMadridSpain

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