The phrase "brief algorithm" is grammatically correct and can be used in written English.
You can use this phrase when describing a concise set of steps or instructions for solving a problem or completing a task. For example: "Before diving into the code, let me first explain the brief algorithm that we will be following to achieve our goal." "The instructor provided us with a brief algorithm for simplifying complex mathematical equations." "Learning how to create a brief algorithm is essential for any programmer."
A brief algorithm of the order statistics-based CDF estimation is given as follows [13].
A brief algorithm has been described below to separate the player exchange events from the high EPR events.
The paper also contains additional discussions regarding canonical representation of stereochemical isomers, and brief algorithm descriptions of the open source LINDES, Java applet, and Open Babel MCDL processing module software packages.
Oriented Fast and Rotated BRIEF (ORB) consists of a fast keypoint detector which is based on the FAST algorithm and binary descriptor which is based on the BRIEF algorithm [9].
The paper also contains some additional discussions regarding canonical representation of stereochemical isomers in MCDL format, and brief algorithm descriptions of the open source LINDES, Java applet, and Open Babel MCDL processing module software packages.
The optimal solutions of m are simply derived from [ 19] (19) m = max b ⊙ χ 4 α 4 − υ 5, 0. The brief algorithm is described in Algorithm 1.
In brief, this algorithm attempts to reduce the influence of the split into proper and calibration sets by performing multiple such splits, each resulting in an inductive conformal predictor, and aggregating the resulting predictions.
In brief, this algorithm starts with an initial area to which neighboring areas are iteratively attached until the addition of any neighboring area fails to increase the magnitude of the local ( {G}_i^{ast } ) of Getis and Ord [5] and Ord and Getis [19].
In brief, the algorithm first distinguishes candidate interactions between a TF and its targets by estimating the expression pairwise mutual information (MI).
In brief, the algorithm utilizes gene expression profiles of cell populations harvested from mouse tissues (mostly leukocytes; e.g., T-cells, DCs, macrophages), and for each population, a set of "signature transcripts" is identified, which consists of transcripts highly expressed in that population relative to normal mouse skin.
In brief, the algorithm can be summarised as follows.
