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We observe that when the number of datasets is small, the difference in the execution times for the two systems is insignificant, but when the retrieved data are moderately large, SemLinker significantly outperforms the BDI Ontology system.
The number of overlapping SNPs between the two datasets is small for both ER-positive and overall breast cancer analyses.
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The number of training cases required for near optimal classification for such datasets is smaller and hence smaller proportions devoted to the training set could be near optimal (for n = 100 - 200).
These datasets are small, and we can compress and reconstruct them on one processor.
Cellucci et al. (2005) show that equal bins can give very poor results, particularly when the datasets are small.
It is hard to preset the number of GMM components especially when datasets are small and have varying sizes among speakers (see the subsequent section 'Speech material').
In general, the overlap between these two datasets was small.
Comparing the relative utility of diagnostic tests is challenging when available datasets are small, partial or incomplete.
Although our nuclear DNA datasets were small, divergence between G. pusilla and G. calmariensis was strongly supported.
While several examples mentioned above have successfully applied topic modeling to genomic datasets, the sizes of the studied datasets were small (less than 100).
The low performance could be due to over-fitting, as the regression trees are rich in expressive power while the numbers of PRDs in the datasets are small.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com