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The Fisher’s Linear Discriminant

  • Conference paper
Combining Soft Computing and Statistical Methods in Data Analysis

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 77))

Abstract

In this paper we have chosen a proper vector b in order to prove that the MSE discriminant function a t Y is directly related to Fisher’s linear discriminant. The Fisher’s criterion is in the range of techniques for performing linear discrimination in the two class case. The Fisher’s linear discriminant is a criterion function that involves all of the samples, while the perceptron criterion function is focussed on the misclassified samples.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Technical University of Civil Engineering of Bucharest, Romania

    Iuliana F. Iatan

Authors
  1. Iuliana F. Iatan
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Editor information

Editors and Affiliations

  1. Research Unit on Intelligent Data Analysis and Graphical Models, European Centre for Soft Computing Edificio Científico-Tecnológico. 3a Planta, C/ Gonzalo Gutiérrez Quirós s/n, 33600, Mieres, Spain

    Christian Borgelt , Gil González-Rodríguez  & Wolfgang Trutschnig ,  & 

  2. Departamento de Estadística e I.O. y D.M., Universidad de Oviedo, Facultad de Ciencias, C/ Calvo Sotelo s/n, 33007, Oviedo, Spain

    María Asunción Lubiano

  3. Departamento de Estadística e I.O. yD.M., Universidad de Oviedo, Facultad de Ciencias, C/ Calvo Sotelo s/n, 33007, Oviedo, Spain

    María Ángeles Gil

  4. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Przemysław Grzegorzewski

  5. Systems Research Institute, Polish Academy of Science, Newelska 6, 01-447, Warsaw, Poland

    Olgierd Hryniewicz

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Iatan, I.F. (2010). The Fisher’s Linear Discriminant. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_43

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  • DOI: https://doi.org/10.1007/978-3-642-14746-3_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14745-6

  • Online ISBN: 978-3-642-14746-3

  • eBook Packages: EngineeringEngineering (R0)

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