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- W24189900 abstract "The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a design sample size that increases exponentially with the number of dimensions. A method was developed that combines classifications from marginal density functions using an additional classifier. Unlike voting methods, this method can select a more appropriate class than the ones selected by the marginal classifiers, thus overriding their decisions. For two classes and two features, this method always demonstrates a probability of error no worse than the probability of error of the best marginal classifier." @default.
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- W24189900 date "2000-05-22" @default.
- W24189900 modified "2023-09-26" @default.
- W24189900 title "Overriding the Experts: A Stacking Method for Combining Marginal Classifiers" @default.
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