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The features are called f k,i), k denoting the song index and i denoting the feature index.
Casting the problem in a binary classification framework, we refer to each protein domain as an instance, with the ith instance consisting of a feature vector x i ∈ [1 × n] and a label y i ∈ {0,1}, with n denoting the feature count.
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where denotes the feature.
The red '+' signs denote the feature points.
Let x denote the feature vector and y the alignment accuracy.
and denote the feature sequence matrices of two signals, where and are the number of frames in signal and, respectively.
Figure 3 The bits extraction framework based on two-dimensional quantization and coding, where D denotes the number of features; K denotes the number of feature pairs; c k denotes the feature index for the kth feature pair (k=1,…,K); s k denotes the corresponding quantized bits.
Figure 6 enumerates the ordered mutual information between each feature and identities, where the horizontal axis presents the mutual information and the vertical axis denotes the feature names.
In Fig. 5, a horizontal axis denotes the feature value and a vertical axis denotes membership belonging to a positive group.
where v i denotes the feature vector of the i t h training sample and the permittivity ε ri is the corresponding target value.
Figure 5 enumerates the ordered mutual information between each feature and activity types, where the horizontal axis presents the mutual information and the vertical axis denotes the feature names.
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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