Different from the AdaBoostSVM in [17] with the fixed training error value (ε th = 0.5), in this paper, we adjust the diversity of the classifier by changing the training error threshold.
Similar to AdaBoostRBFSVM, we adjust the diversity of the classifier by changing the training error threshold.
The diversity of the AdaBoostSVM is regulated by changing the training error threshold. .
When they blurred the images by systematically changing the focus error in the computer simulation and tested the response of the filters, the researchers found that they could predict the exact amount of focus error by the pattern of response they observed in the feature detectors.
Instead of freeing y5's measurement error variance at the behest of the modification index, the researcher might decide to drop y6 and thereby permit changing the fixed error variance on y5 to locate η3A.
We see no scientific justification for changing the Type I error rate associated with the difference test from the customary 5% level to 10%, as proposed.
Moreover, beyond the error thresholds, changing the quality factor of some sequences modifies the population distribution at the error catastrophe, displacing it towards the flatter regions of the mutant phenotype.
Since learning is associated with the synaptic weights, back propagation algorithms minimize the error by changing the weights following each pass through the network.
Changing the a priori data error does not influence shear wave velocity models significantly.
