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Our method adopts a forward selection algorithm to determine feature correspondences needed for estimating the transformation model.
In order to take into account the relationships between detected visual words we apply a graph matching algorithm on each pair of sequences to determine feature level similarities.
We record the accuracy, precision, recall, specificity and (F_{1}) score for each of the experiments we conduct and compare these results to evaluate the particular feature set that we considered as well as determine feature importance.
The Feature extraction software (V. 9.5.1) from Agilent was used to automatically find and place microarray grids, reject outlier pixels, accurately determine feature intensities and ratios, flag outlier pixels, and calculate statistical confidences.
In the learning process, we build 100 trees and determine feature numbers by optimizing F-score.
After washing, the arrays were scanned at PMT levels 350 setting using GenePix 4200AL scanner (Molecular Device Inc ., the Feature extraction software (V. 9.5.1) from Agilent was used to automatically find and place microarray grids, reject outlier pixels, accurately determine feature intensities and ratios, flag outlier pixels, and calculate statistical confidences.
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We determine features both in the depth maps and point clouds to extract discriminative fall descriptors.
Sequences that formed reliable hairpin structures were further analyzed to determine features such as thermodynamics stability, presence of star sequences, and phylogenetic conservation [18].
The Random Forests classification method (Breiman, 2001) was used to determine features explaining the difference between FB and NFB either alone or in combination with other factors.
We want to determine features of the components that are perceived by participants to be valuable, and those which are in need of further development.
Our goal is to determine features that are most informative in discriminating one class from the rest even if they are quite uninformative in other 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