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The correctness of classification in physiological versus pathological patterns obtained by LFM prediction was 79.4%, 76.5%, and 94.1%, respectively, for hip, knee, and ankle data (in mean 83.3%).
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A consistency check based on double counting is carried out to gain confidence in the correctness of the classification.
The direction of the gradient update depends on the correctness of the classification using a nearest neighbourhood rule in Euclidean space.
Tested on this database, the algorithm proved to be highly accurate both in the correctness of the classification and the segmentation accuracy.
Subsequently, analysis was performed to find how correctness of emotion classification depends on the number and the order of the parameters in the input feature vector and on the computation complexity.
The correctness of a classification is determined by adding new feature information that characterize classes and instances.
In this process the gait transitions could be traced and confirmed easily, proving correctness of gait classification.
Criteria used to evaluate the significance of the alignments, and therefore the correctness of the classification, were: the conservation of gene structure between human and cow, the parameters of alignments and the presence of genscan predictions, ESTs, mRNAs and UniGene features, aligned alongside with the query sequence.
This way of illustration helps to judge the correctness of our initial classification, and the classification by cluster analysis.
And it seems that elements of that type would mandate the correctness of one or another classification.
Classification accuracy is the overall correctness of the classifier and is calculated as the sum of correct cancer classifications divided by the total number of classifications.
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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