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We averaged the performance of the BLM by the weights of the number of corresponding interactions, generating an AUC of 0.9676.
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For a fair comparison, we averaged the performances of the two experts and reported the results in Table 1.
The "true" accuracy of classification was estimated by averaging the performance of the 2 classifiers for each subject.
AUC was calculated by averaging the performance scores of ten trials of cross validation.
Results were averaged over the performance of the SVM on the left-out participant.
On the average, the performance of SAM is 7.65 times faster than the original bitmap AC.
Then, it builds classifier models for each set before averaging the performance of all the classifiers.
On average, the performance of activities within this category occupied 67.37 sec (Me = 16, SD = 142.91).
On average, the performance of our unweighted method in this benchmark is comparable to SIFT.
On average, the performance of the activities within this category occupied 104.53 sec (Me = 42.6, SD = 207.72).
On average, the performance of the activities within category 4 occupied 114.07 sec (Me = 32, SD = 338.26).
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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