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• W4253776206 abstract "Abstract In this article, we consider discriminant analysis where classification is undertaken in the context where there are a number of classes and the observed data (the so‐called training data) are of known classification with respect to these classes. We consider both parametric and nonparametric approaches to the formation of allocation rules from the available training data. The coverage starts with Fisher's linear discriminant function, which was the initial attempt to perform discriminant analysis in its modern guise. The estimation of the error rates of the sample‐based rules so formed are considered. Particular attention is given to the classification of high‐dimensional data where the number of training observations is small relative to the large number of available variables." @default.
• W4253776206 created "2022-05-12" @default.
• W4253776206 creator A5057375178 @default.
• W4253776206 date "2012-08-31" @default.
• W4253776206 modified "2023-10-16" @default.
• W4253776206 title "Discriminant Analysis" @default.
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• W4253776206 doi "https://doi.org/10.1002/9780470057339.vad023.pub2" @default.
• W4253776206 hasPublicationYear "2012" @default.
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