<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The empirical Bayes Gaussian rule, which in the normal case yields good values of the probability of total error, may yield high values of the maximum probability error. From this point of view the presented modified version of the classification rule of Broffitt, Randles and Hogg appears to be superior. The modification included in this paper is termed as a WR method, and the choice of its weights is discussed. The mentioned methods are also compared with the K nearest neighbours classification rule.</p></abstract>

The empirical Bayes Gaussian rule, which in the normal case yields good values of the probability of total error, may yield high values of the maximum probability error. From this point of view the presented modified version of the classification rule of Broffitt, Randles and Hogg appears to be superior. The modification included in this paper is termed as a WR method, and the choice of its weights is discussed. The mentioned methods are also compared with the K nearest neighbours classification rule.

On the Discriminant Analysis in the 2-Populations Case

Institute of Measurement Science, Slovak Academy of Sciences, Dúbravská cesta 9, 84104 Bratislava, Slovakia

Measurement Science Review

{"article-title":"On the Discriminant Analysis in the 2-Populations Case"}

The empirical Bayes Gaussian rule, which in the normal case yields good values of the probability of total error, may yield high values of the maximum...

On the Discriminant Analysis in the 2-Populations Case

Online veröffentlicht: 25. Juni 2008

Seitenbereich: 50 - 52

DOI: https://doi.org/10.2478/v10048-008-0013-z

Schlüsselwörter
discriminant analysis, nearest neighbour rule, Gaussian Bayes classification rule, maximum probability error

This content is open access.

On the Discriminant Analysis in the 2-Populations Case

Online veröffentlicht: 25. Juni 2008

Seitenbereich: 50 - 52

DOI: https://doi.org/10.2478/v10048-008-0013-z

Schlüsselwörterdiscriminant analysis, nearest neighbour rule, Gaussian Bayes classification rule, maximum probability error

This content is open access.

Schlüsselwörter
discriminant analysis, nearest neighbour rule, Gaussian Bayes classification rule, maximum probability error