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The Tangent Classifier

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  • José R. Berrendero
  • Javier Cárcamo

Abstract

Given a classifier, we describe a general method to construct a simple linear classification rule. This rule, called the tangent classifier , is obtained by computing the tangent hyperplane to the separation boundary of the groups (generated by the initial classifier) at a certain point. When applied to a quadratic region, the tangent classifier has a neat closed-form expression. We discuss various examples and the application of this new linear classifier in two situations under which standard rules may fail: when there is a fraction of outliers in the training sample and when the dimension of the data is large in comparison with the sample size.

Suggested Citation

  • José R. Berrendero & Javier Cárcamo, 2012. "The Tangent Classifier," The American Statistician, Taylor & Francis Journals, vol. 66(3), pages 185-194, August.
    • Handle: RePEc:taf:amstat:v:66:y:2012:i:3:p:185-194
    DOI: 10.1080/00031305.2012.710511
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    Cited by:

    1. Hea-Jung Kim, 2015. "A best linear threshold classification with scale mixture of skew normal populations," Computational Statistics, Springer, vol. 30(1), pages 1-28, March.

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