The phrase "algorithm metrics" is correct and usable in written English.
It can be used when discussing the measurements or standards used to evaluate the performance or effectiveness of algorithms.
Example: "In our analysis, we will focus on the algorithm metrics to determine which model performs best under various conditions."
Alternatives: "algorithm performance indicators" or "algorithm evaluation criteria".
Reordering algorithms and metrics: Many algorithms can be applied to matrix reordering.
Following image processing with our algorithm, available metrics to quantify neurite alignment include angular histograms, percent of neurite segments in a given direction, and mean neurite angle.
The learning algorithm performance metrics considered here is the distance d t (in dB) between the secondary SINRs obtained with the multi-agent Q-learning algorithm and the secondary SINRs given by the optimal allocation algorithm: d t = ∑ l = 1 N SINR t, l s - SINR ^ t, l s 2 t ∈ { 1, …, K } (34).
To evaluate the performance of the proposed algorithm, two metrics are used in Tables 3 and 4: the average BD-rate (BDBR) [31] difference between the proposed algorithm and HM6.0, and the time reduction ratio which is defined as ΔT = T HM − T p T HM × 100%%. (17).
Furthermore, the development of recommendation systems is a difficult task due to the implementation of complex algorithms and metrics.
Table 13 shows the algorithms' performance metrics.
Papers presenting, validating, or applying models and/or computational techniques, new algorithms, dynamic metrics for networks and complex systems and papers comparing, contrasting and docking computational models are strongly encouraged.
This technique, used in learning algorithms whose metrics can be found in [13] and Bradzil et al. [14], is now widespread in many areas and applications.
In this section, we discuss specific algorithms and metrics extracted using the reconstructed map, which are used for the adaptive rate control of per-node sensing in RACS.
