Research ArticleOpen AccessGoogle Scholar indexed
Statistical Classification Using the Maximum Function
Université de Moncton, Moncton, Canada
University of Economics and Law, Hochiminh City, Vietnam
University of Finance and Marketing, Hochiminh City, VietNam
- 1 Université de Moncton, Moncton, Canada
- 2 University of Economics and Law, Hochiminh City, Vietnam
- 3 University of Finance and Marketing, Hochiminh City, VietNam
Open Journal of Statistics·Volume 05 (2015)·Pages 665–679·Published 11 December 2015·DOI10.4236/ojs.2015.57068
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Abstract
The maximum of k numerical functions defined on , , by , is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theoretical results on this function, and then its application in classification using a computer program we have developed. This approach leads to clear decisions, even in cases where the extension to several classes of Fisher’s linear discriminant function fails to be effective.
KeywordsMaximumDiscriminant FunctionPattern ClassificationNormal DistributionBayes ErrorL1-NormLinearQuadraticSpace Curves
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