Sentence examples for the maximum relevance from inspiring English sources

To circumvent this, we invoked the maximum relevance minimum redundancy (mRMR) model to aid in selecting the most relevant features in terms of class discrimination and the most compact or non-redundant features to represent the face mesh models.

The Maximum Relevance Minimum Redundancy (mRMR) method combining with Incremental Feature Selection (IFS) and Feature Forward Selection (FFS) are then applied for feature selection.

The first term in the equation is the maximum relevance condition, and the second term is the minimum redundancy condition.

In Equation (11), the maximum relevance score is added to normalize the gap between the relevance scores of different articles.

In this study, we utilized the maximum relevance minimum redundancy mRMR algorithm, which reduces the features' dimensions by selecting the most relevant features while removing the redundant ones.

This scheme, termed as minimum-redundancy-maximum-relevance selection [42], has been found to be more powerful than the maximum relevance selection.

Peng et. al. [28] presented the Maximum-Relevance-Minimum-Redundancy (MRMR) criterion, which is in principle equivalent to MIFS with the (beta = 1/ n-1)), where n is the number of selected features in the current subset.

(2) I (g i, c ) = ∫ p (g i, c ) ln ⁡ p (g i, c ) p (g i ) p (c ) d g i d c The Maximum-Relevance method selects the top m genes in the descent order of I(g i, c), i.e. the best m individual features correlated to the class labels.

The maximum relevancy minimum redundancy (mRMR) method [ 14] is used to rank features based on the criterion of maximum relevance to the target and the minimum redundancy between features.

The features that can best discriminate the four groups of patients were ranked by the mRMR (Maximum Relevance & Minimum Redundancy) [10] principle.

The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method.