Exact(1)
The number of samples in each pool dictated the amount each sample contributes to the pool.
Similar(59)
Each sample contributed an equivalent amount of protein to the total pooled protein.
The saliva specimens for each class were pooled and each sample contributed to make the pool with an equal amount of protein.
This system has the potential to model the dependencies between contextual factors and acoustic features such that each training sample contributes to train multiple sets of model parameters.
This problem stems from the fact that the hard decision tree structure assigns each model parameter to exactly one cluster (corresponding to a small part of the large contextual space): each training sample contributes to the estimation of only one set of model parameters (one mean vector and one covariance matrix).
The contextual additive model is able to exploit contextual factors more efficiently, because mean vectors and covariance matrices of the predicted distributions are the sum of mean vectors and covariance matrices of the additive components [45]: each training sample contributes to more than one model parameter.
Furthermore, in this structure, each training data sample contributes to modeling multiple mean vectors and covariance matrices.
A fourth 'Full' pool (N = 89) was formed by combining equimolar amounts of each of the Mothers, Fathers and Offspring DNA pools so that each individual sample contributed 150 ng to the final pool; this equated to combining 87 μl of Mothers pool with 90 μl from the Fathers pool and 90 μl from the Offspring pool.
The fact that these contextual factors are non-overlapped leads to the insufficient context generalization, because this fact makes each training sample contribute to the model of only one leaf and only one Gaussian distribution.
Ligations were pooled such that each offspring sample contributed an equal amount of DNA.
Each bootstrap sample contributed one cut-point estimate, so that the standard deviation of the 200 cut-point estimates was used as the bootstrap estimator of the standard deviation (SDB) for the estimated cut-point.
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