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For toy datasets generated from the OVA model, the minimum predictor set size necessary to differentiate among the K classes is K - 1.
Goal setting was addressed in two of the items in the self-control scale, which emerged as a minimum predictor of the propensity to adopt EBP.
[ 19] Initiative and self-discipline were consistent with the behaviors described in the self-management scale, which emerged as a minimum predictor of the propensity to adopt EBP.
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However, it is shown in [1] that even when the vocal tract filter follows an actual all-pole model, this criterion of goodness is not fulfilled by the classical minimum variance predictor.
The rounding operators being omitted, it is readily shown that the minimum variance predictors must satisfy the well-known Yule-Walker equations.
Details of minimum error predictors for all samples and subsets.
For each subset, the genes listed are those used in the minimum error rate predictor (except for the 'all' category, for which a seven-gene predictor was selected to observe a longer gene list to allow comparison with the other gene lists).
The sparse nature of the data and the minimum number of predictor variables means that these variables are not normally distributed and are considered logged variables.
It is therefore convenient to select the best minimum subset of predictor variables (also named features).
This step was repeated until there were only two SNPs remaining in the model (the minimum number of predictor variables that can be used).
A minimum subset of predictor variables (also called 'features') that provides high generalization power to the Bayes linear classifier should, therefore, be sought using an optimization criterion.
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