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Exact(12)
Results reveal the relation between the feature vector and classification accuracy.
Examples of the Euclidean distances between the feature vector h z (ref) and h z (test) obtained from genuine signatures and forgeries are shown in Figure11.
Second, the mean squared error between the feature vector estimate and the true state is reduced compared to the estimate provided by the standard particle filter.
The average MI between the feature vector and the speaker identity is shown in Figure3b (bottom), again as a function of scale.b.b
The observation shows that the correlations between the feature vector are lost when the number of sub-bands is more numerous.
The average MI (taken as the mean MI across all the frequency bands for a given scale) between the feature vector and the speech message is shown in Figure3b (top) as a function of scale.
Similar(48)
Fisher vector encoding computes the first and second order differences between the feature vectors and Gaussians.
After feature extraction the chosen distance measure between the feature vectors of the example and each database sample is calculated.
Equation (a) computes the local distance between the feature vectors of the frame of segment and the current input time.
The similarity between the feature vectors is computed based on their Euclidean distance in the feature space.
For the loss function f, we use the L2 distance between the feature vectors, (f cdot)=||cdot ||^{2}_{2}).
More suggestions(18)
between the feature adaptation
between the motility vector
between the feature size
between the mosquito vector
between the triplet vector
between the beamlet vector
between the feature word
between the feature centre
between the pixel vector
between the trial vector
between the feature set
between the vHMEC vector
between the feature f
between the feature number
between the displacement vector
between the wave vector
between the feature selection
between the impedance vector
Write better and faster with AI suggestions while staying true to your unique style.
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