Exact(1)
They were employed to check on the generalization capability of the trained neural networks and unbiased split of data for linear and nonlinear part of load displacement relationships (Reitermanova 2010).
Similar(59)
Among the most notably of these devices is a stable monolithic device based on CIGS cells interconnected in series, which reached 10% solar-to-hydrogen efficiency for unbiased water splitting.
In order to perform unbiased comparisons, we split the training data into a smaller training set of 2000 utterances and a hold-out set of 1233 utterances.
To achieve an unbiased evaluation, we split the S1914 data set randomly into training and independent test sets with a ratio of 2 : 1.
The minimum band gap for a single-junction device in order to withhold unbiased overall water splitting is considered to be at least 2.0 eV.
The new method utilizes Stein unbiased risk estimate estimator to split the signal, the Lipschitz exponents to identify noise elements and a heuristic approach for the signal reconstruction.
It was not possible to use unbiased region identification in the Split half analysis because, when region selection was based on only 16 or 17 subjects, there was insufficient power to locate effects that were significant after a whole brain correction for multiple comparisons (see discussion in Poldrack and Mumford, 2009).
However, as AAV split vector genome concatemerization is unbiased, the efficiency of functional transgene reconstruction is low.
The proportion of variation captured by PC1, which represents the taurine indicine split, decreased with increased gene flow in the unbiased ascertainment treatments, whereas this relationship was removed or reversed in the biased treatments (Additional file 1: Table S5).
We selected this previously described approach[ 33] instead of split sample internal validation because it provides an more accurate, unbiased estimate of performance in external cohorts.
Theoretical results showed that the scalar-weighted rank statistics considered are unbiased and that their asymptotic variance is not affected by the splitting of the sample in m groups when optimal scalar weights are used, but is there a measurable loss on finite samples?
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