Sentence examples for methods which try from inspiring English sources

Meta-heuristic algorithms are problem-solving methods which try to find good-enough solutions to very hard optimization problems, at a reasonable computation time, where classical approaches fail, or cannot even been applied.

Most of the approaches to the progressive collapse analysis of steel frames have focused so far on computational methods which try to capture the solution of the system responding to localized damage.

(Active learning methods, which try to infer which anomalies would contain the most useful information and could thus serve to guide the anomaly unveiling, show further, though fairly minor, improvement).

One well-known reverse engineering approach is that of top-down methods, which try to infer network properties based on the observed global input-output-response.

Several variance-stabilization and normalization transformation methods, which try to transform expression values to be equal variance and normally distributed for each gene, have been proposed [ 19- 23].

While TREAT p-values can be used with most multiple testing adjustment schemes, including Benjamini and Hochberg (1995), they do not fulfil the assumptions of some methods which try to estimate the proportion of truly null genes such as Ferkingstad et al. (2005).

k-Means is one of the most popular clustering methods, which tries to minimize the sum of the squared distance within the clusters [48].

PU-learning is an iterative method which tries to identify a set of reliably negative instances in the unlabeled data.

This paper proposes a method which tries to reduce overhead and message drop while increasing message delivery ratio.

BMO is a population-based search method which tries to imitate the mating ways of bird species for designing optimum searching techniques.

More sophisticated methods are the "greedy" method which combines loci if they exceed a minimal overlap (or fall below a maximal distance of each other) and the "minmax" method which tries to find maximal extensions by merging overlapping loci within predefined minimal and maximal sizes.