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The statistical model was learnt from a small set of chains with evidence for being unstructured in isolation, but structured in complex with their partners.
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In parallel, a nominal model was learned from vehicles which complied with the traffic rules.
A standard QSAR model was learned on the training data set using ε-support vector regression and the respective kernel.
These models are used to conduct simulated currency trading in the year following the one in which the model was learned.
In [21], the inverse dynamic model was learned from the data by training a backward nonlinear predictor (e.g., neural network).
Predictive model was learned on N tr patients.
The prediction model was learned by the support vector regression (SVR) algorithm.
A multivariable Cox regression model was learned for testing the independent prognostic relevance of the risk score and other variables.
An 18-state model was learned from all binarized data and was used to produce segmentations based on the most likely state assignment of the model.
The phasing model is learnt using an analysis-synthesis loop that iterates HMM estimations and forced alignments with the original data.
The parameters of this model are learned from a set of demonstrations performed by a human.
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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