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In order to forecast future trend of the topic, we use the following benchmark methods: Using All, Highest Correlated field, Highest Correlated field plus one.
Table 2 Verification rate at and in parenthesis the relative contribution that automatic registration has in comparison with manual registration on FRGC [4, Experiment ], comparing all registration methods using all face classifiers.
Results ranged from F-measures of 18% to 96% for pure dictionary-based approaches, from 54% to 97% for dictionary-based approaches with dictionary enrichment from corpus, and from 68% to 93% for machine learning methods, using all features.
We applied the same methods using all samples to determine coefficients for use in independent datasets.
We train all the methods using all gene associations for 3209 OMIM diseases collected until August 2011.
Paired (i.e. two, correlated) samples t-testing is used here for comparison of methods using all the data.
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The above three methods use all the relays since relay selection is not used.
The CC method uses only 284 complete-case observations and the other two methods use all 418 observations.
The methods uses all available information (i.e., multiple markers and pedigrees).
Sequence signature-based methods use all reads and can potentially reveal more accurate relationships of samples.
Whole-genome regression methods use all available quality filtered SNPs into a model, contrary to accommodating only validated SNPs from exonic or coding regions.
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