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- W2012405890 abstract "In previous work [1], we developed a method for finding the top-performing Gaussian mixture models for the language recognition. This method orders the models from best-performing to worst-performing using calculated dispersion measures. Multiple dispersion measurements are used to produce multiple rankings of the models, which are combined to produce a ranking from which the top-performing models can be extracted. This method has reduced model testing time, since researchers can determine the top-performing models without evaluating the entire population of models. In this paper we demonstrate the ability of our ranking rule to find the top-performing models for different data sets and performance measures. The performance of our ranking rule is also compared to existing ordinal ranking rules: Kohler [2], Arrow & Raynaud [2], Borda [3], and Copeland [3]." @default.
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- W2012405890 date "2013-03-01" @default.
- W2012405890 modified "2023-09-29" @default.
- W2012405890 title "Using an ordinal ranking rule to find the top-performing Gaussian mixture models for language recognition" @default.
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- W2012405890 doi "https://doi.org/10.1109/ciss.2013.6552299" @default.
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