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The proposed method is compared to other methods using several examples.
Most, if not all, of the studies related to SoH estimation focus on the measurement of a new parameter/physical phenomena related to SoH, or development of new statistical/computational methods using several parameters.
In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background.
We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest.
We demonstrated the utility of LPv for evaluating different analytical methods using several published microarray datasets.
We calculated the percent of variance removed by normalization for the RA method and FA methods (using several different score estimators) for flies in LD and DD (Table 1).
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Some methods use several stereo frames and attempt to ensure temporal coherence.
Other methods use several alignment features as evidence of an indel.
These functions were determined by a semi-empirical method using several reference spectra.
The architectures generated by the conditional Monte Carlo method are compared to those from a full MC method using several qualifiers.
We validate the method using several two-dimensional numerical examples that include design-dependent gravitational loading (self-weight) and time-dependent applied loads.
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