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Here we denote the mean vector of Class1 as μ1 and the mean vector of Class2 as μ2.
where ( {overline{mathbf{x}}}_i )is the mean vector of ith class' training samples, and ( overline{mathbf{x}} ) is the mean vector of total training samples.
Ci is the mean vector of ith class and Cj is the mean vector of jth class, both from the same classifier.
This results in a zero mean vector of observations [6] and successively a zero mean matrix.
where (underline {a}_{n}underline {m}_{n}) is the mean vector of the matched filter output.
where is the mean vector of the th component and is a identity matrix [17].
The mean vector of these DCT coefficients is considered as a model for closed lips.
The mean vector of each Gaussian component corresponds to a clustering center of the feature space.
where X ̄ ( f, ω ) is the mean vector of X f, ω).
Let denote the mean vector of a trained acoustic model in a speech recognizer obtained from the clean speech data.
The mean vector of the optimal adapted model is estimated in a maximum-likelihood sense from the reverberant models.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com