Sentence examples for minimum redundancy approach from inspiring English sources

A recent study[22] proposed maximum distance minimum redundancy approach to generate initial negative training datasets and predicted non-coding RNAs from unlabeled data, which may be an useful way for the generation of negative training examples with high confidence and could be extended to the investigation of PTM site prediction modeling.

Various versions of the Minimum Redundancy Maximum Relevance approach have been described in references as a supervised variable selection methodology tailor-made for classification purposes, while its primary disadvantage has been explained as its high sensitivity to the presence of outlying measurements [ 15].

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups.

There are many feature evaluation approaches available, and the minimum redundancy maximum relevance (mRMR) algorithm [65], which can find the optimal features with minimum redundancy, was used in this study.

MRMR, known as Minimum Redundancy Maximum Relevance, however, attempts to detect those redundant subsets, find them out, and delete them.

Minimum redundancy array.

The sequential forward selection, genetic and maximum relevance minimum redundancy algorithms are used for a precise selection of features.

So, a method based on the Minimum Redundancy Linear Array was then adopted.

We considered that each encoder has an extra output (minimum redundancy).

Minimum redundancy arrays (MRAs) and minimum hole arrays (MHAs) are two common classes of nonuniform linear arrays [6 9].

This paper proposes real linear transceivers employing minimum redundancy, unlike the standard block transceivers that require, at least, L elements of redundancy, where L is the channel order.