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The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions.
These methods assess each feature individually by its ability to discriminate the classes.
Second, we utilize a statistical control approach to assess each feature's unique contribution to fixation selection.
The importance function from the randomForest R package was used to assess each feature's individual performance after training the model with the full training set.
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Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature's unique contribution to fixation selection.
The generalized linear mixed modeling approach used here allows for assessing each feature's unique contribution to fixation selection, and its relative importance.
On the one hand, most studies on reputation-based cooperation have dealt with assessment rules that share the feature that assessors thoroughly assess each play.
These studies used all histone modification ChIP-Seq data available for either human or mouse ES cells to predict TF binding sites without assessing the predictive value of each feature.
To assess this feature, pre-contrast images need to be considered.
We used a random-forest based algorithm, Boruta39, to assess the relevance of each feature by ranking them based on their predictive importance (Fig. 2a) (Methods).
In this study, we explore diagnosis of Traditional Chinese Medicine based on lip Images by combining imaging features from color component, textural and moment features, and assess the significance of each feature in classification.
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