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Several strategies could be adopted to classify the runs as committed to a state.
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A fuzzy system is used to classify the running mode and identify the operational and process transients.
The leave one out algorithm incorporating PAM was run to classify the velocities into two groups.
For the phenotypic evaluation a disease index called the "G2 index" was calculated to classify the genotypes on a scale running from 0 to 9; from the most resistant (G2 index = 0) to the most susceptible varieties (G2 index = 9).
Cufflinks assemblies for all samples were then merged using Cuffmerge v2.0.2 which also runs Cuffcompare internally to classify the transcripts.
The data collected were then run with the SAS code and its ability to classify the cases tested.
Classify the fire.
For each docking run, we classified the models generated with and without constraints according to the ligand and interface root mean square deviations (rmsd).
Next, 3-8 iteratiofs of the k-means algorithm was run in order to pre-classify the feature space.
In our application, when the system runs, we have no access to an oracle to pre-classify the instances.
The 95% confidence interval for the difference of the proportion correctly classified above runs from SBaSeTraM being 1.03% better, to GMATIM being 0.93% better.
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