Sentence examples for mutual information score from inspiring English sources

We transformed the text into a database containing questions, answers and metadata about the answers, then extracted four- and five-word phrases and calculated a PMI (pointwise mutual information) score for each.

(NMI) Normalized Mutual Information score: is an normalization of the Mutual Information score that measure the mutual information between two clusterings.

The feature selection criterion based on mutual information score is commonly adopted in literature.

To solve the problem, one can assume each feature is independent of all other features, and rank the features in descending order according to their individual mutual information score (I(X_i, Y )).

This difference is measured by the mutual information score.

We hypothesize that a higher mutual information score implies that the personal genome is more susceptible to a particular disease than other diseases.

To mitigate the inconsistent feature selection issue of regularization, especially the lasso method [9], the framework preanalyzes all features to rank informative features based on mutual information scores [10].

Figure 13.7 shows terms with high mutual information scores for the six classes in Figure 13.1.

Figure 13.7: Features with high mutual information scores for six Reuters-RCV1 classes.

The minimal redundancy between variables is calculated by finding the mutual information scores between variables.

These relationships were identified using point-wise mutual information scores (PMI) and then used to construct a pathogen similarity tree, which has several interesting features (Fig. 5).