Sentence examples for algorithm transformation from inspiring English sources

The principle of algorithm transformation from GA to EACA is when the objective function value is smaller than the default value after five generations of changing continuously in GA optimization process, and then the control parameter combinations can be regarded as the pheromone for EACA.

Table 1 Summary of the proposed approaches   Algorithm Transformation Relevance measure Method 1 Algorithm 1 1D–PCA ρ lc Method 2 Algorithm 1 1D–PCA ρ su Method 3 Algorithm 1 1D–PLS ρ lc Method 4 Algorithm 1 1D–PLS ρ su Method 5 Algorithm 2 2D–PCA ρ lc Method 6 Algorithm 2 2D–PCA ρ su Method 7 Algorithm 2 2D–PLS ρ lc Method 8 Algorithm 2 2D–PLS ρ su.

Peak localization was determined from the MNI coordinates and identified by the Talairach and Tournoux atlas after a non-linear algorithm transformation (MRC Cognition and Brain Sciences Unit, Cambridge, UK).

Each ANN had a distinct combination of training algorithm, transformation method, and number of neurons, but both, BRANN and SCGANN, had an input of 3K SNPs.

To this end, the experimental analysis evaluates the performance of 12 multi-label learning algorithms (transformation, adaptation, and ensemble-based) on a series of 16 multi-label datasets with and without supervised and unsupervised discretization, showing that LAIM discretization improves the performance for many algorithms and measures.

The use of hypergraphs in the proposed algorithm of transformation reinforces the generation process.

On this basis, an algorithm of transformation of PSN into a list of concepts and semantic links is presented.

As for the "asymmetric" histories, the algorithm of transformation of the stress history (because of its global expected value different from zero) gives similar results to the algorithm of amplitude transformation of cycles.

Table 1 summarizes the eight different combinations accomplished for the proposed approaches, including the algorithm, the transformation and the relevance measure used in each case.

The next step of the self-taught learning algorithm involves transformation of the labeled data into new feature vectors using the dictionary learned at the previous step.

Therefore, the main problem with anchor-free methods is an additional algorithm for transformation from the relative to the absolute coordinates.