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The most supported value of K was estimated using the CV procedure, as implemented in ADMIXTURE (Alexander et al. 2009; Alexander and Lange 2011).
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The main data set was divided into ten training and test files following the tenfold cross-validation (10cv) procedure using the cv tools library from CRAN.
You can sync it with other gear using the CV, gate, and clock inputs.
In a first step, the individual classifiers were built and optimized separately using the same CV procedure described before.
In contrast, if all steps leading to the prediction rules are performed in each CV iteration using only the corresponding training set, the CV procedure is "full CV".
In the CV procedure, eight subsets were used for training, and one subset in turn was left out for testing.
In reality, On-Point had no employees and had used the CVs of potential employees.
The average number of updates required by the SM-AP algorithm using the general CV, the simple choice CV, and the noise CV are 35, 9.7, and 3.6%, respectively, implying that the last two CVs also have lower computational cost.
We also repeated the same experiments using the LOO-CV, LDO-CV and LTO-CV strategies (Supplementary Tables S10 S17).
This was achieved using the following procedure.
Using the procedure rank in SAS 9.2 (SAS Institute, Cary, NC), each CV received a rank according to the size of the CV value.
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