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We compared Random Forest and three geostatistical approaches for the interpolation of SOC in unsampled locations.
In another study, Rossi et al. [21] analyzed and compared random utility models and fuzzy logic models for representing gap acceptance behavior using data from driving simulator experiments.
For each of the models we generated and compared, random test sets combined randomly against random test sets that had their values combined by the pairwise strategy.
Compared with the traditional plane detection algorithm Compared Random sample consensus (RANSAC) [10], the 3D Hough transform performed faster and was more stable.
Stehman compared random vs. regular sampling locations for other types of problems, for example in [33], and concluded that systematic, or regular, locations tend to be more accurate.
Second, we compared random priming (RP) labeling with nick translation (NT) labeling and found that the RP labeling gave fewer false-positive gains and fewer false-negative losses in the detection of one-copy changes.
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We compare random projection, principal component analysis and diffusion map for anomaly detection.
This research compares random effects, zero inflated, and zero inflated random effects models using a full Bayes hierarchical approach.
The effect is statistically assessed on a phase of specified fatigue strength by comparing random fatigue lives.
By then comparing random and clustered uptake simulations, it was shown that some local grids might see a substantial increase of peak loads faster than expected.
First, the full set of fixed effects from the global model was assumed for comparing random effects.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com