1967
DOI: 10.2307/2528418
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An Almost Unbiased Method of Obtaining Confidence Intervals for the Probability of Misclassification in Discriminant Analysis

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Cited by 431 publications
(176 citation statements)
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“…Selected characters were then subjected to principle component analysis (PCA) to reveal patterns of the species, and followed by DFA to compute the classification success. Misclassification rates of DFA were calculated using holdout cross validation procedures proposed by Lachenbruch (1967). The kappa (κ) statistics was used to determine the improvement over chance of the percentcorrect classification rates (Titus et al, 1984).…”
Section: Resultsmentioning
confidence: 99%
“…Selected characters were then subjected to principle component analysis (PCA) to reveal patterns of the species, and followed by DFA to compute the classification success. Misclassification rates of DFA were calculated using holdout cross validation procedures proposed by Lachenbruch (1967). The kappa (κ) statistics was used to determine the improvement over chance of the percentcorrect classification rates (Titus et al, 1984).…”
Section: Resultsmentioning
confidence: 99%
“…4a-c below). We then performed a Bleave-one-out cross-validation^(LOOCV) procedure (see Geisser, 1975;Lachenbruch, 1967;Miller, 1974;Stone, 1974; for a review, see Arlot & Celisse, 2010) on these points using a standard feedforward-backpropagation (FFBP) network (Rumelhart, McClelland, & the PDP Research Group, 1986). This analysis worked as follows.…”
Section: Scanpath Comparisonmentioning
confidence: 99%
“…The reliability of the discrimination of shape variables was calculated by leaveone-out cross-validation (e.g. LACHENBRUCH, 1967). DA was repeated on non-allometric shape variables (sizecorrected) to evaluate the sexual dimorphism without the allometric effect.…”
Section: Cheliped Allometry and Sexual Dimorphismmentioning
confidence: 99%