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The associations represented by the learned models are correlated with the traditional measure of LD D′.
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By using the learned models to filter the results, the distribution of SL and BPP is changed such that it better fits the expectations for a pre-miRNAs for human.
An explanation for this behavior is that genetic markers near the center of the blocks are highly conserved in the population (had minimal recombination), which is reflected in the learned models by their tendency to be more causative than dependent nodes.
Obviously these auxiliary training examples need to be good estimates of the actual interaction status of the corresponding object pairs, for expanding the training sets by wrong examples could further worsen the learned models.
Furthermore, the activation maps computed by CBVw and BOLD data show increased agreement when using the learned models, lending more confidence to their accuracy.
These methods learn from some example interactions, and predict the missing ones based on the learned models.
Then, the learned models are used to bootstrap the system.
The learned models are then used to detect abnormal tracks or objects.
MDS performed data mining and applied the learned models.
We translate the hypothesis into a prior that will bias the learned models.
By explicitly including a node representing a hidden variable in a network – even if there are no recorded data on that variable – the learned models are likely to be simpler and less prone to overfitting [ 65].
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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