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A demonstration with artificial data of the sensitivity graph transduction exhibits for certain initial label settings.
Paper contains details on experimenting with proposed approaches on artificial data of hand-written character recognition and comparison of both methods.
Figure 3A shows the instability values for clustering the artificial data of Figures 1 and 2 in dependence on the number of clusters.
Figure 3B shows the quality calculated for clustering the artificial data of Figures 1 and 2 in dependence on the number of clusters and for varying datasets.
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The paper also illustrates the use of artificial data to debug and compare these algorithms.
An interesting test for putative occurrence of long-branch effects is the simulation of sequence evolution along topologies, alternatively with and without junction of long branch taxa, followed by an analysis of the artificial data to check for deviations from the true tree [ 117].
Several methods of creating artificial data for training the approximating model were proposed and compared on different classification tasks.
In simulation studies, we relied on existing benchmark datasets for epistatic detection to generate artificial data for evaluating the effectiveness of our method.
The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability.
We trained the Kstar classification model on this artificial data set of 602 cases and then applied so trained model to the original 86 cases (48 malignant and 38 benign).
To verify our approach, we constructed artificial data sets, consisting of sets of strings in which phenotypic traits were acquired in a single ordering.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com