Sentence examples for training variance from inspiring English sources

To improve training prediction accuracy, iterative bootstrap aggregation, or bagging, combined with out of bag error estimation was used to reduce the training variance [ 53].

While the variability of sequence frequency decreased significantly throughout training (variance: F8,152 = 4.450, p < 0.0001, Figure 2C; Fano factor: F8,152 = 5.343, p < 0.0001, Figure 2E), the variability of sequence duration significantly increased (variance: F8,152 = 11.15, p < 0.0001, Figure 2D; Fano factor: F8,152 = 16.86, p < 0.0001, Figure 2F).

To estimate the training effect, variance components for each period were estimated using a similar model in which the random effects were grouped by period instead of setting.

For training satisfaction the variance explained was 42% at t2, 56% at t3 and 63% at t4; for learning motivation 49% at t2 and 65% at t3.

More complex models have greater representation power (low bias) but overfit to the particular training set (high variance).

This "one-way" training is at variance with the "two-way education" identified in a previous study as a prerequisite for successful collaboration [ 18].

In fact, when subtracting from the total effect of training (c) the variance accounted for by the mediator, the remaining direct effect showed a coefficient (c1) of the sign opposite to that of the total effect (c).

Specifically, the means of the interferer GMM are scaled by an additive factor of β= log(10−R/10) since log-spectral vectors are used in training, while the variances will remain unchanged because β is an additive factor.

The objective of MCVELM is to minimize both the intra-class variance of training data in the decision space and the training errors.

Similar to MCVSVM, the objective of MCVELM is to minimize both the intra-class variance of training data in the decision space and the training errors.

This evaluation criterion may suffer from the variance in training data with a limited sample size and result in unstable generalization error when the trained model is applied to an unknown dataset.