On the relationship between the Support Vector Machine for classification and sparsified Fisher's Linear Discriminant

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Abstract

We show that the orientation and location of the separating hyperplane for 2-class supervised pattern classification obtained by the Support Vector Machine (SVM) proposed by Vapnik and his colleagues, is equivalent to the solution obtained by Fisher's Linear Discriminant on the set of Support Vectors. In other words, SVM can be seen as a way to `sparsify' Fisher's Linear Discriminant in order to obtain the most generalizing classification from the training set.

Original languageAmerican English
Pages (from-to)129-139
Number of pages11
JournalNeural Processing Letters
Volume9
Issue number2
DOIs
StatePublished - 1999

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