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Refined IBD is computationally efficient and can be used on large data sets.
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Despite its high prediction accuracy and its ability to avoid over-fitting of the data, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used on large scale data because of the prohibitive cost.
RASCAL gives similar classification performance to PRW but at lower computational expense making it suitable for use on large data sets and particularly for the ensemble schemes outlined in this paper.
Its fast computation allows its use on large data sets as well.
Several nonprobabilistic IBD-detection methods have been developed for use on large data sets.
Sales of Microsoft's network server products, like its SQL server database and versions of Windows used on larger computers in corporate data centers, increased 20percentto to $2.3 billion.
Neighbor Joining [ 19, 35], on the other hand, although it constructs the tree in a step-wise fashion by utilizing the input distance matrix and by joining the closest neighbors, is more popular in phylogenetic analysis as it can be used on very large data sets for which other methods (e.g. minimum evolution, maximum parsimony, maximum likelihood) are computationally prohibitive.
However, we may now study these patterns using computational methods on large data sets.
Further work will involve development of the algorithm for use on larger data sets.
Data compression techniques had been used for applying the models on large data, that is, for large scale classification, dictionary learning while for large scale regression pre-clustering approach had been applied.
Moreover, Checkmol is very slow and is impractical to use on very large data sets.
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